Hiatus and Hiatus Resolution in Québécois French

Anne-Bridget St-Amand

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Linguistics University of Toronto

Anne-Bridget St-Amand

Doctor of Philosophy

Department of Linguistics University of Toronto

2012 Abstract

This thesis is about vowel-vowel sequences across word boundaries in Québécois French (QF).

QF has a number of phonological processes that seem motivated by hiatus avoidance, yet hiatus is tolerated in many instances as well. This research is about why hiatus is tolerated in QF, why it is avoided, and what grammatical models can account for the relevant processes.

My work is intended as a contribution to the study of how best to account for variation and opacity within current grammatical models, and as a contribution to the study of the QF vowel system. The data are drawn from a corpus constructed from recordings of web extras for a

Québécois reality television series. The data primarily come from a single speaker to ensure that any variation in the data truly represents intra-grammar variation, but data from other speakers are used as safeguard. Through the use of quantitative data as a means of investigating problems in theoretical phonology, the thesis is also meant to contribute to methodological discussions and discussions about the relationship between phonetics and phonology.

I propose that the patterns of hiatus and hiatus resolution in QF are best modeled through three sets of constraints organized in a serial manner. This proposal is based on the claims that the data show evidence for an anti-hiatus constraint, for feature-based analysis, for stochastic modeling, and for multiple levels.

The proposed model combines insights from Stochastic Optimality Theory (Boersma & Hayes

2001), multi-level Optimality Theory (Kiparsky 2000, 2010; Rubach 2000), and the Contrastivist

Hypothesis (Dresher & Rice 2002, Dresher 2009, Hall 2007). Within the model, the first constraint set targets the smallest prosodic constituents and produces categorical outputs, the second applies to intermediate-sized constituents and can model optionality, and the third handles the largest prosodic constituents and produces complex patterns of variability.

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Acknowledgments

This thesis would not have been possible without Elan Dresher’s support, insight and infinite wealth of knowledge about all aspects of phonology. I am also profoundly thankful to Keren Rice and Anne-Marie Brousseau, who were both exceedingly generous in providing me with thorough and invaluable feedback throughout the writing process. I would also like to thank the other members of my examining committee, Yoonjung Kang and Arto Anttila, for extremely useful and thoughtful comments.

I would also like to thank the other members of the Department of Linguistics at the University of Toronto for creating a vibrant, challenging and friendly environment. I am particularly thankful to past and present members of Phon group for wide-ranging discussions, and help in clarifying some of the ideas in this thesis.

I am also grateful to the faculty of the Department of Linguistics at the University of Ottawa. My studies there laid the foundation for this research.

I would also like to acknowledge the financial support of the Social Sciences and Humanities Research Council of Canada, the Ontario Graduate Scholarship Program, and the University of Toronto.

Je tiens aussi à remercier profondément mes parents et ma soeur pour leur appui et leur amour. My friends and other family members were also key in keeping me going: I am very grateful to them all.

Finally, I would like to thank Sam for everything.

Table of Contents

Chapter 1 Introduction...... 1

1.1 Hiatus literature ...... 2

1.2 Methods ...... 5

1.2.1 Theoretical frameworks ...... 6

1.2.1.1 Variationism ...... 6

1.2.1.2 Theoretical Phonology ...... 7

1.2.2 Data source ...... 8

1.2.2.1 Corpus creation and data extraction ...... 8

1.2.2.2 Data presentation ...... 10

1.3 Overview of proposal ...... 11

1.3.1 Anti-hiatus constraint ...... 11

1.3.2 Feature-based analysis ...... 12

1.3.3 Stochastic evaluation ...... 12

1.3.4 Multiple levels ...... 13

1.4 Thesis organization ...... 13

Chapter 2 Hiatus Not Expected ...... 14

2.1 Fixed final consonants ...... 14

2.1.1 Methodology and results for typical cases ...... 15

2.1.2 Vowel deletion following a fixed final consonant ...... 16

2.1.2.1 Avec ...... 16

2.1.3 Not always a fixed final consonant: elle ...... 19

2.2 No schwa ...... 20

2.2.1 General pattern ...... 20

2.2.2 Vowel deletion following a clitic ...... 23

2.2.3 Schwa instead of no schwa ...... 24

2.3 Liaison ...... 25

2.3.1 Liaison context ...... 26

2.3.2 Obligatory liaison ...... 27

2.3.3 Other liaison ...... 29

2.3.4 Variation in liaison ...... 29

2.3.5 Liaison as anti-hiatus repair ...... 31

2.3.6 The beginnings of an analysis ...... 32

2.3.6.1 Nature of the liaison consonant ...... 33

2.3.6.2 Constraints for categorical underlying liaison ...... 35

2.3.6.3 Constraints for categorical epenthetic liaison ...... 37

2.3.6.3.1 Pronouns ...... 38

2.3.6.4 Constraints for variable epenthetic liaison ...... 40

2.3.6.5 Consonant choice ...... 41

2.4 What is needed going forward ...... 44

Chapter 3 Hiatus Expected ...... 45

3.1 Regular hiatus ...... 45

3.1.1 Telling hiatus, diphthongs and deleted vowels apart ...... 47

3.1.2 Vowel deletion really? ...... 50

3.1.2.1 Clear acoustic evidence for devoicing? ...... 52

3.1.2.2 Subtle acoustic evidence for devoicing? ...... 54

3.1.2.3 Conclusions about devoicing ...... 56

3.1.2.3.1 The relationship between phonetics and phonology ...... 58

3.1.3 Preliminary statistics ...... 59

3.1.4 The beginnings of an analysis ...... 61

3.1.5 Hiatus versus diphthong and glide ...... 62

3.1.6 Deletion and Coalescence ...... 63

3.1.6.1 Contrastive Hierarchy ...... 64

3.1.6.2 The coalescence data ...... 65

3.1.6.3 Binary vs. privative features ...... 66

3.1.6.4 Further issues with privative features ...... 72

3.1.6.5 Choosing the best set of privative features ...... 73

3.1.6.6 Successful feature hierarchy ...... 75

3.2 H-aspiré ...... 79

3.2.1 H-aspiré data ...... 80

3.2.2 Analyzing h-aspiré ...... 82

3.2.3 The details of the analysis ...... 83

3.2.3.1 Example tableaux ...... 84

3.2.3.2 Remaining words not h-aspiré? ...... 86

3.3 What is needed going forward ...... 86

Chapter 4 Variation ...... 88

4.1 Models for Accounting for Variation ...... 91

4.1.1 Before Optimality Theory ...... 91

4.1.2 After Optimality Theory ...... 93

4.1.3 Stochastic OT and the Gradual Learning Algorithm ...... 94

4.1.3.1 Stochastic OT, GLA and OTSoft: the basics ...... 95

4.1.3.2 How the GLA settles on an optimal model ...... 96

4.2 Preliminary Run ...... 98

4.2.1 Max [V] ...... 98

4.2.2 Core constraints ...... 99

4.2.3 Exclusions ...... 100

4.2.4 Results of the preliminary run ...... 101

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4.2.5 Interpreting Error Rates ...... 102

4.2.6 Structured variation ...... 105

4.2.7 Criteria for best constraint set ...... 106

4.3 Best Constraint Set ...... 106

4.3.1 MAX [F] constraints: strong vs. weak...... 108

4.3.2 Feature Hierarchy ...... 109

4.3.3 Constraints that were not selected ...... 111

4.4 Summary ...... 112

Chapter 5 Opacity ...... 114

5.1 Opacity before and after the era of Optimality Theory ...... 114

5.2 Opaque processes in the data ...... 116

5.2.1 Assibilation ...... 117

5.2.2 Liaison ...... 120

5.2.3 H-aspiré ...... 121

5.3 A stochastic multi-stratal grammar ...... 122

5.4 Defining the levels ...... 124

5.4.1 Level 1: Clitic Group ...... 124

5.4.2 Level 2: Phonological Phrase ...... 126

5.4.2.1 Optional processes in level 2 ...... 127

5.4.2.2 Duke-of-York derivations? ...... 128

5.4.2.3 Categorical processes in level 2...... 129

5.4.2.4 Constraints and comparison with level 1 ...... 130

5.4.3 Level 3: Intonational Phrase ...... 131

5.4.3.1 Constraints and comparison with other levels ...... 132

5.5 Example Tableaux ...... 133

5.5.1 Glide Formation ...... 134

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5.5.2 Diphthong Formation ...... 137

5.5.3 Liaison ...... 139

5.5.4 Coalescence ...... 145

5.5.5 Deletion ...... 147

5.5.6 H-aspiré ...... 149

5.5.7 Assibilation ...... 151

5.6 Summary ...... 153

Chapter 6 Conclusion ...... 155

6.1 Methodology ...... 155

6.2 Phonology of QF ...... 156

6.3 Proposed model ...... 157

6.4 Learnability ...... 159

References ...... 160

List of Tables

Table 1. Comparison of full and partial extraction ...... 9

Table 2. Initial vowel deletion for avec ‘with’ ...... 17

Table 3. Coding for liaison context ...... 27

Table 4. Breakdown of optional liaison tokens according to first word ...... 30

Table 5. Formant values for /CVV/ and /CV/ sequences ...... 55

Table 6. Duration comparison for [ sѐ] ...... 56

Table 7. Centre of gravity comparison for [ sѐ] and [ si] ...... 56

Table 8. Deletion rate of high and non-high vowels...... 57

Table 9. Cases of coalescence in the data ...... 66

Table 10. Phonetic features for the QF vowel inventory ...... 67

Table 11. Non-contrastive specifications used for CoalMiner ...... 74

Table 12. Best contrastive specifications using privative features ...... 77

Table 13. Comparison of h-aspiré, V-initial, and C-initial forms...... 80

Table 14. Data for h-aspiré ...... 81

Table 15. Rates of glide/diphthong formation according to presence of high vowel ...... 99

Table 16. Comparison of / Ϫa/ and / aϪ/ ...... 100

Table 17. Results of preliminary OTSoft run ...... 102

Table 18. Three-way comparison of output rates for three most frequent forms ...... 105

Table 19. Best constraint set for vowel deletion ...... 107

Table 20. Bivalent feature set for successful hierarchy ...... 112

Table 21. Privative features superior for coalescence but ultimately unsuccessful...... 167

Table 22. Possible feature orderings for Table 21 ...... 169

List of Tableaux

Tableau 1. Parles-en ...... 36

Tableau 2. Quelques uns ...... 36

Tableau 3. Ancien amant ...... 37

Tableau 4. Un étang ...... 38

Tableau 5. On a ...... 38

Tableau 6. Vous avez...... 38

Tableau 7. Ça a ...... 39

Tableau 8. J’ai...... 39

Tableau 9. Tu as...... 39

Tableau 10. Il a ...... 40

Tableau 11. Ils ont ...... 40

Tableau 12. Elle a ...... 40

Tableau 13. Est un ...... 41

Tableau 14. Multi-level approach to h-aspiré item: le homard ...... 85

Tableau 15. Multi-level approach to h-aspiré item: un homard ...... 85

Tableau 16. Multi-level approach to h-aspiré item: du homard ...... 85

Tableau 17. Level 1 for glide formation ...... 135

Tableau 18. Level 2 for glide formation ...... 135

Tableau 19. Level 3 for glide formation ...... 136

Tableau 20. Level 1 for diphthong formation...... 138

Tableau 21. Level 2 for diphthong formation...... 138

Tableau 22. Level 3 for diphthong formation...... 138

Tableau 23. Obligatory liaison with epenthesis...... 140

Tableau 24. Obligatory liaison with underlying consonant ...... 141

Tableau 25. No liaison with ils ...... 143

Tableau 26. Optional liaison ...... 144

Tableau 27. Coalescence ...... 146

Tableau 28. V1 deletion...... 147

Tableau 29. V2 deletion...... 148

Tableau 30. Hiatus with h-aspiré ...... 149

Tableau 31. Vowel deletion with h-aspiré ...... 150

Tableau 32. Glide insertion with h-aspiré ...... 151

Tableau 33. Assibilation and coalescence ...... 152

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List of Figures

Figure 1. Liaison results according to liaison context...... 27

Figure 2. Example of hiatus in Praat ...... 48

Figure 3. Example of diphthong in Praat ...... 48

Figure 4. Syllable structure for diphthong and glide...... 49

Figure 5. Example of V2 deletion in Praat ...... 50

Figure 6. Devoicing, deletion and opacity ...... 52

Figure 7. Spectrogram and waveform showing high vowel deletion...... 53

Figure 8. Spectrogram and waveform showing high vowel devoicing ...... 53

Figure 9. Distribution of hiatus expected tokens ...... 59

Figure 10. Distribution of vowel deletion tokens...... 60

Figure 11. binary [low] > [ATR] ...... 69

Figure 12. binary [ATR] > [low] ...... 69

Figure 13. Coalescence with binary [low] > [ATR] ...... 70

Figure 14. Coalescence with binary [ATR] > [low] ...... 70

Figure 15. Hypothetical privative feature hierarchy ...... 71

Figure 16. Coalescence with hypothetical privative feature hierarchy ...... 72

Figure 17. Contrastive hierarchy for best constraint set ...... 77

Figure 18. Coalescences with best set of feature specifications ...... 78

Figure 19. Hypothetical relationship between *VV and MAX V leading to deletion ...... 95

Figure 20. Hypothetical relationship between *VV and MAX V leading to hiatus ...... 96

Figure 21. Constraint formulations for preliminary OTSoft run...... 102

Figure 22. Average error per candidate according to # of most frequent underlying forms... 104

Figure 23. Constraint formulations for best constraint set for vowel deletion...... 107

Figure 24. Strong and weak MAX [F] ...... 109

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Figure 25. Contrastive hierarchy for best constraint set ...... 110

Figure 26. Kim’s (2001) phonetically based account of assibilation ...... 118

Figure 27. Constraint formulations for Level 1: Clitic Group ...... 125

Figure 28. Constraint ranking for Level 1: Clitic Group ...... 126

Figure 29. Example of prosodic structure ...... 127

Figure 30. Constraint formulations for Level 2: Phonological Phrase ...... 130

Figure 31. Constraint ranking for Level 2: Phonological Phrase ...... 131

Figure 32. Constraint formulations for Level 3: Intonational Phrase ...... 133

Figure 33. Most likely ranking of *VV and V=NUCLEUS resulting in attested form for glide formation...... 136

Figure 34. Less likely ranking of *VV and V=N UCLEUS resulting in possible form for glide formation...... 137

Figure 35. Inputs and outputs in proposed model ...... 158

Figure 36. Plausible feature hierarchy for privative features superior for coalescence but ultimately unsuccessful ...... 168

Figure 37. Coalescences with privative features superior for coalescence but ultimately unsuccessful ...... 170

Figure 38. Plausible feature hierarchy for privative features superior for coalescence but ultimately unsuccessful, with bivalent [back] ...... 173

Figure 39. Additional successful coalescences with bivalent [back] ...... 173

Figure 40. Worse outcome for other coalescences if no Contrastive Hierarchy ...... 174

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List of Appendices

Appendix A : Average F1 (on y-axis) and F2 (on x-axis) for the main speaker ...... 166

Appendix B : Ultimately unsuccessful feature hierarchy ...... 167

Appendix C : Bivalent [back] ...... 172

Appendix D : Benefits of Contrastive Hierarchy...... 174

Appendix E : Possible feature orderings for Table 12 ...... 175

Appendix F : Results of runs with most frequent underlying sequences ...... 177

Appendix G : Input vs. Generated Frequencies for Best Constraint Set ...... 185

Appendix H : Summary of Grammars Tested with GLA in OTSoft ...... 193

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Chapter 1 Introduction

When a French word that normally ends with a vowel is followed by a word that normally begins with a vowel, the outcome may or may not be two vowels in a row. This thesis is about what determines whether hiatus does indeed surface in such contexts for one variety of French in particular, Québécois French (QF). In QF, hiatus is often tolerated, but sometimes much seems to be done to avoid it, as the contrast between the examples in 1 and 2 suggests.

1. [...] parce que eux ont vu le- le show. [kœøѐ] (=/ k#ø#ѐ/) ‘because (them) they saw the- the show’ (I/50/23,24)

2. [...] pis ça (l’)a une petite crème. [sa l ௙n] (=/ sa#Ϫ#yn/) ‘and it has a little cream’ (I/43/1,2)

Both examples are from actual utterances spoken by a single individual, 1 but the surface form in 1 has a sequence of three vowels in a row, while in 2 an underlying sequence with three vowels in a row results in no hiatus at all in the surface form. For the example in 1, the first surface vowel is taken to be epenthetic in the analysis advocated for here. While this is not a view shared by everyone, there is in any case no indication of hiatus avoidance. In the case of 2, one of the vowels deletes and a consonant is inserted, in what seems to be a concerted effort to avoid hiatus.

The apparent contradiction between the behaviour exhibited in 1 and 2 is at the heart of this thesis: why is hiatus tolerated in QF, why is it avoided, and what grammatical models can account for the relevant processes? The thesis is intended as a contribution to the study of how best to account for variation and opacity within current grammatical models, as well as to the study of the vowel system of QF. Through its use of quantitative data as a means of investigating problems in theoretical phonology, it is also meant to contribute to a wider methodological discussion, as well as a discussion on the relationship between phonetics and phonology. The proposed analysis accounts for cases where hiatus surfaces in QF and cases where hiatus is

1 See 1.2.2.2 for notes on data presentation.

2 resolved within a single model. This model borrows from Stochastic Optimality Theory (Boersma & Hayes 2001) to handle variation, from multi-level Optimality Theory (Kiparsky 2000, 2010; Rubach 2000) to handle opacity, and from the Contrastivist Hypothesis (Dresher & Rice 2002, Dresher 2009, Hall 2007) to analyze the QF vowel inventory. The model has three constraint levels that are linked in a serial manner and defined prosodically. The last level has stochastic evaluation, and all levels contain constraints that target contrastive features.

In this introduction, I present an overview of my conclusions, but before doing so I discuss the existing literature on hiatus as well as the data and methodology I use. In section 1.1, I review previous accounts of hiatus and hiatus resolution in QF. In section 1.2, I introduce the data and methods this work relies on. In section 1.3, I present an overview of the proposal contained within the thesis, while in section 1.4, I describe the organization of the thesis.

1.1 Hiatus literature

Hiatus and hiatus avoidance are central to nearly every classic problem in French phonology. Liaison, h-aspiré, and schwa are some of the most (if not the most) studied phenomena, both in Standard French (SF) and QF. For all of these, the tolerance or avoidance of vowel-vowel sequences is central. For liaison, the typical case involves a consonant that would not otherwise appear surfacing between two vowels, as in the two examples in 3. As the comparison points in 4 show, enfant ‘child’ is vowel-initial in citation form, and neither determiner in 3 has a consonant when followed by a consonant-initial word, meaning that / n/ and / z/ surface in 3 because of the hiatus context.

3. un enfant les enfants [œnafa] [lezafa] ‘a child’ ‘the children’

4. enfant(s) un garçon les garçons [afa] [œցaలsѐ] [leցaలsѐ] ‘child(ren)’ ‘a boy’ ‘the boys’

In the case of h-aspiré, a small group of vowel-initial items are exceptions to the pattern in 3 in that they impose hiatus where it would normally be avoided, as 5 illustrates.

5. héros un héros les héros [eలo] [œeలo] [leeలo] ‘hero’ ‘a hero’ ‘the heroes’

Finally, hiatus also plays a part in the complex pattern that accounts for when and where schwa surfaces. For instance, with the definite determiner le , schwa surfaces (optionally in QF) with consonant-initial words, never surfaces with vowel-initial words, and always surfaces with h- aspiré words, as in 6.

6. le garçon l’enfant le héros [lœցaలsѐ] [lafa] [lœeలo] ‘the boy’ ‘the child’ ‘the hero’

For liaison and h-aspiré, the extent to which hiatus ought to be explicitly incorporated into analyzing the process is a key component to the debates reviewed below. For schwa, the role of hiatus and hiatus avoidance is less central, but as the examples above illustrate, all of the phenomena are inter-connected.

In the case of liaison, which involves the pronunciation of a consonant between two words that in isolation would not contain this consonant, the role that hiatus avoidance might play is clear. In the vast majority of liaison contexts, the liaison consonant breaks up what would otherwise be a vowel-vowel sequence. Whether this hiatus avoidance is in fact what motivates the appearance of the liaison consonant has been important within the longstanding debates about how to define the contexts that trigger liaison and the nature of the liaison consonant.

Morin (2005) provides a detailed account of the debate about the role of hiatus avoidance in the liaison literature. He describes a turn towards hiatus-based explanations, which followed the very first analyses of liaison (such as Gougenheim 1935, 1938) where avoiding hiatus was not cited as a motivation. He cites recent analyses as continuing to rely on hiatus avoidance to explain liaison, in particular Tranel (2000), who forcefully argues that liaison is due to a universal anti- hiatus tendency. This type of analysis is sometimes accompanied by, and sometimes replaced by, the claim that French has a tendency to avoid empty onsets (as in Steriade 1999 and Perlmutter 1996, among others). For Morin, both types of analyses are equally problematic, and he argues against seeing such well-formedness considerations as functionally motivating liaison. He shows that the historical trajectory of liaison is not compatible with the view that it is primarily an anti- hiatus repair, and points to many examples where liaison does not serve to break up a sequence

4 of two vowels. Morin’s argument against liaison as an anti-hiatus mechanism is closely linked to his argument against treating liaison as a single unified process. But if liaison is taken to be heterogeneous, and the existence of competing forces leading to apparent exceptions is recognized, the possibility that hiatus avoidance could be at play for some aspects of liaison does not seem to be ruled out by Morin’s work.

In the case of the group of words known as h-aspiré, hiatus is always an important issue and is sometimes at the very crux of the proposed analyses. This class of phonetically vowel-initial words behaves abnormally with respect to a number of sandhi phenomena and most strikingly imposes hiatus in liaison and other contexts where hiatus would normally not be tolerated. This means that hiatus figures in any analysis, but for some the exceptional vowel-vowel sequences are simply consequences of some underlying characteristic. Many proposals explain the surface hiatus through segments or empty elements that intervene between the vowels underlyingly, but subsequently delete (Schane 1968, Dell 1973, Encrevé 1988, among others). For such analyses, while the presence of hiatus is clearly anomalous, no specific mechanism referring to hiatus in particular is required. However, a number of proposals that explicitly rest on the role of hiatus within the phonology of French have been put forward.

Klausenburger’s (1977) work on h-aspiré situates the phenomenon within a larger view of French as representing a tension between hiatus and non-hiatus conspiracies. On the side of hiatus, h-aspiré is joined by two kinds of cases where liaison could in principle occur but doesn’t: prohibited and optional liaison. On the side of non-hiatus, he also places two types of liaison (obligatory and ‘false’ (i.e. non-standard) liaison) as well as elision of schwa. Klausenburger proposes to account for the behaviour of the h-aspiré items by diacritically marking this lexical class with [+hiatus], making his analysis amongst the most explicit in linking hiatus and h-aspiré. Although the resulting analysis is very different from the one advocated for here, his insight about the tension between competing forces fits exceedingly well and seems in keeping with more contemporary approaches to phonology.

More recently, Boersma’s (2007) work on h-aspiré relies heavily on the role of hiatus in the system. In this case, hiatus and/or hiatus avoidance is not an underlying property of h-aspiré words, but instead is a cue to h-aspiré status. Boersma follows many others in claiming that these words have an underlying initial consonant, which is often unpronounced. But in his analysis,

5 listeners rely on the surface vowel-vowel sequences to map to an h-aspiré lexical item. For Boersma, it is hiatus at the surface that is crucial for explaining the behaviour of h-aspiré.

Finally, hiatus has also played a role in debates about schwa, especially within early rule-based analyses. The unstressed vowel, which in QF is always pronounced [ œ] (Côté 2005b), appears variably in a wide range of phonological contexts, but seldom adjacent to a vowel. A huge literature has emerged which seeks to account for the obligatory presence of schwa in some contexts, its absence from others, and the patterns of its variable surfacing in others. The early influential analysis of Dell (1973) posited underlying schwas for all of the words that sometimes had schwa in them, with subsequent deletion explaining why they do not surface. This meant that much deletion of schwa was necessary, and in particular when vowel adjacent. Hiatus avoidance was a natural motivation for these deletion rules. Hiatus ceased to play a pivotal role in subsequent analyses, which tended to explain the presence of schwa as intervening in undesirable consonant clusters, either for reasons of syllable well-formedness (Morin 1974, Anderson 1982, Tranel 1987 among others) or perception (Côté 2000). Just as for liaison and h-aspiré, the role of hiatus avoidance in determining the distribution of schwa ranges from large to nonexistent depending on the analysis.

Despite the contentiousness of the theoretical debates, from a descriptive standpoint, many have recognized a tendency to avoid hiatus in QF. Walker (1984: 122) sums up the situation as “CF [Canadian French], as do many languages, appears to have constraints against sequences of vowels. While these constraints are not absolute (hiatus does occur in CF, and frequently), there is clear pressure to reduce VV sequences [...]”. It is these constraints and their effects that this thesis seeks to explore.

1.2 Methods

The research in this dissertation is corpus-driven and includes significant quantitative and statistical components. This section provides a general description of the methodology adopted, but statistical and other tools are described as needed within the thesis. In 1.2.1, I describe the frameworks that informed the methodology, and in 1.2.2 the data.

1.2.1 Theoretical frameworks

This research is influenced by variationist sociolinguistics, as described in 1.2.1.1, and seeks to contribute to debates within theoretical phonology, as described in 1.2.1.2.

1.2.1.1 Variationism

This work borrows some central tenets of variationist sociolinguistics, most importantly the assumption of the inherent variability of language (Labov 1969). At the basis of this is the idea that one linguistic function may be associated with more than one form, and vice versa. This remains the case even if an analysis were able to account for every possible factor that might favour one form over another. Eliminating the effect of stylistic, social, situational, and any other such factors will not lead to a situation where only one-to-one relationships exist between form and function. It is because of this view that I consider it a necessity to adopt a grammatical model that can deal with variability.

I also adopt many of the methods of variationism, in particular the notion of the variable context (Tagliamonte 2006 among others). I analyze the presence of a given form within a certain context by examining similar cases where the form could have appeared but did not. This allows me to look at the conditioning factors for that form: what in the environment favours it, and what disfavours it. Linguistic factors that I determine influence phonological processes in this way need to be accounted for within the analysis.

A case from the morphosyntax of QF can provide a simple example of how this will work. In Poplack & St-Amand (2006), we examine the trajectory of the negative marker ne , which obligatorily marks negation in SF but is almost always absent in QF. We define the variable context as every sentence with a post-verbal negative marker, i.e. any sentence in which ne could have appeared regardless of whether it did. Calculating rates of ne in relation to all sentences the marker could have surfaced in makes for a much more reliable comparison than, for instance, simply comparing total numbers of ne . Following this method, one of our main findings is that the contemporary use of ne is strongly correlated with specific topics such as religion and ‘soap- box speech’, where it serves as a marker of formality. But in keeping with the concept of the inherent variability of language, this does not imply that all cases of ne can be attributed to this role, or that all sentences of this type have ne in them. This effect contributes strongly to the probability that ne will appear, but it does not wholly determine it. The study of phonological

7 variation here will follow this basic template of identifying a variable context, and using it to pinpoint factors that make one form more likely than another.

Because of the focus on finding a formal model that accounts for variation, it is essential that any variability within the data truly belong to a single grammar. Where variationist studies often involve the data from many speakers being pooled together, this seems a risky move. It has been well attested that small differences can exist between speakers of what appears to be a single variety (see Auger 2001 for an illustration and excellent discussion of this). For this reason, combining speakers seems as though it could give the impression of variation where none exists. In order to ensure that intra-grammar variation is truly in play, data from a single speaker form the bulk of the research data (see 1.2.2 below).

1.2.1.2 Theoretical Phonology

It is a reality of corpus-based linguistics that it is (nearly) impossible to have access to numerous identical phonological strings. In an ideal world, I would have data from an individual speaker repeating exactly the same thing, with exactly the same intonation and at exactly the same rate of speech, but within the prized naturalistic setting. In the absence of this, when a form deviates from what would have been expected, it is not clear what to do. While a single form may be anomalous for extra-grammatical reasons, such as performance errors, it may correspond to something real in the phonology. Despite making use of the statistical tools used frequently in quantitative approaches to linguistics, I adopt a more traditional view than many variationist and data-driven studies in that I seek to account for any form (with respect to hiatus) that would be possible for the speakers I look at. The goal, first and foremost, is to come up with a way to model a grammar within the mind of an individual speaker.

In order to arrive at a grammatical model that fits with the data, I use Optimality Theory (OT: Prince & Smolensky 1993/2004) as a starting point. The final proposal extends standard OT in two main directions: the model has a probabilistic component and multiple levels of evaluation. I also adopt the view that phonological features are organized in a contrastive hierarchy and that only contrastive features are active (Dresher 2009, Dresher & Rice 2002).

1.2.2 Data source

The data I rely on are from web extras for the Québécois reality television series Occupation double from 2006. 2 The series was a dating-based competition, with rounds of eliminations leading to one man and one woman being declared the joint winners of a house. The female contestants shared accommodations, as did the male contestants. The web extras were offered as a bonus to viewers and mostly involve the participants in mundane activities such as eating breakfast and chatting on the porch. I chose these recordings in the hopes that any concern that speakers would modify the way they spoke because they were performing for the camera would be lessened by the fact that these speakers would not be expecting these trivial conversations to be showcased on the television series. The style of speech is indeed informal and natural sounding.

Given the focus on intra-grammar variation, a large portion of the data used for the thesis is from one of the young women participants. I also collected a substantial amount of data for two additional female speakers to use as comparison points. Data from some of the men as well as additional women are also available in cases where they were in conversation with one of the three main speakers.

1.2.2.1 Corpus creation and data extraction

The data comes from 64 audio recordings: 52 for the main speaker, with 6 more each for the two additional speakers. The recordings were converted to .wav files and analyzed with Praat (Boersma & Weenink 2006). The median recording length is about five minutes, with the shortest being about 20 seconds and many lasting about ten minutes. Each recording is transcribed orthographically.

The identification of tokens that were appropriate for studying hiatus and hiatus resolution proceeded in two stages. In the initial stage of data extraction, every word that was phonetically vowel initial was identified. It was transcribed phonetically, as was the word that preceeded it.

2 The material was originally online but is no longer available: I will provide audio files upon request.

This became a token in what I will refer to as the ‘full extraction’ phase. Eight audio recordings were the objects of this full extraction phase. This yielded 497 tokens. The motivation for the full extraction phase was to define the potential sites for hiatus and hiatus resolution processes across word boundaries as broadly as possible. The expectation would be that when a vowel-initial word is preceded by a consonant-final word, hiatus would be impossible, but it was necessary to test this intuition. Through the full extraction phase, I confirmed that there were no unusual patterns that unexpectedly yielded hiatus, and many of the results in Chapter 2 are based on this first stage of extraction. Because this phase involved many similar and identical tokens that did not directly have to do with hiatus and hiatus resolution, I proceeded to a second more selective stage of token extraction.

In the second stage of extraction, which I will refer to as the ‘partial extraction’ phase, only tokens that were likelier candidates for hiatus or one of the anti-hiatus processes were extracted. For this phase, a token was defined as a phonetically vowel-initial word preceded by a phonetically vowel-final word. All such tokens were extracted for the remaining audio recordings. Table 1 below gives an example for this partial extraction, in comparison with the full extraction phase, for one sentence in the data (in 7: no phonetic transcription is given, but Table 1 has what is necessary).

7. Quand j’étais à Paris là, oh my God, moi p’is mon ex, on bad trippait là. ‘When I was in Paris, oh my God, my ex and I, we were freaking out.’ 3 (I/43/27-9)

Table 1. Comparison of full and partial extraction orthography word 1 in isolation word 2 in isolation full extraction partial extraction j’étais Ћ ϯtϯ étais à ϯtϯ a là, oh lϪ o mon ex mѐ ϯks ex on ϯks ѐ

This example sentence would have yielded five tokens if it had been part of the full extraction phase, but only three for the partial extraction phase (which it was actually part of). All of the

3 Là (literally ‘there’) used as a filler word as it is here is often difficult to translate appropriately: where this is the case, it is omitted in the English version.

10 word 2 isolation forms are vowel initial: this is how all tokens were initially identified in either phase. For the full extraction phase, the form that word 1 takes in isolation is irrelevant for inclusion. For partial extraction, only cases where word 1 ends in a vowel are included, which is why the two cases that are highly unlikely to lead to hiatus are excluded.

There are a few minor exceptions where tokens in which word 1 was consonant final were also extracted during the second phase: these are discussed in the thesis as they become relevant. The partial extraction phase yielded 1081 tokens for the main speaker, and 176 and 190 for the two additional speakers. Tokens were on average listened to twice, with a portion of tokens being listened to by others. This resulted in some changes to the phonetic transcriptions, but it did not seem that mistakes or disagreements over transcription corresponded to any particular bias. A token was as likely for instance to be recategorized as having hiatus rather than involving hiatus avoidance as it was to be judged to not be a case of hiatus even though it was labeled as such.

1.2.2.2 Data presentation

Throughout the thesis, examples drawn from the corpus are provided in three lines, as in example 1 repeated below as 8.

8. [...] parce que eux ont vu le- le show. [kœø ѐ] (=/ k#ø#ѐ/) ‘because (them) they saw the- the show’ (I/50/23,24)

The first line is French orthography and the second gives the phonetic transcription along with the assumed underlying form. 4 Only the crucial portion of the excerpt is transcribed phonetically; where the orthographic transcription provides a larger context, the words with a phonetic transcription are in bold. The segments I consider epenthetic are underlined in the phonetic transcription: see 2.2 and 2.3 for the rationale for categorizing them as such. An English translation, which is not fully literal but where disfluencies and other relevant aspects are shown,

4 In providing underlying forms, I focus on making the processes that are discussed in the thesis clearer. Some aspects of QF phonology, related for instance to consonant and vowel quality, are ignored in the underlying forms. In general, the underlying forms do not stray from the surface forms unless there is motivation in the data.

11 is in the third line. The English translation is followed by a bracketed set of references to speaker, file number and token number.

1.3 Overview of proposal

In this thesis, I propose that the patterns of hiatus and hiatus resolution in QF are best modeled through three sets of constraints organized in a serial manner. The first constraint set targets the smallest prosodic constituents and produces categorical outputs, the second applies to intermediate-sized constituents and can model optionality, and the third handles the largest prosodic constituents and produces complex patterns of variability. This proposal is based on four main claims: that the data show evidence 1) for an anti-hiatus constraint, 2) for feature- based analysis, 3) for stochastic modeling, and 4) for multiple levels. These claims are briefly discussed in turn in the subsections below.

1.3.1 Anti-hiatus constraint

The data support the existence of an anti-hiatus constraint, which is instantiated here as *VV (two vowels must not appear in a row). There are two main forms of evidence that support this claim, the first from liaison and the second from vowel deletion. I argue that the first of these processes involves consonant epenthesis (rather than, for instance, the non-deletion of a latent consonant), while in the second one of the two vowels in a row is deleted. This is just what would be expected given the existence of an anti-hiatus constraint: vowel-vowel sequences can be repaired by inserting a consonant between the vowels, or by deleting a vowel.

In the case of liaison, I argue that the data support a view of the process in QF as being on the one hand productive and motivated by hiatus avoidance, but on the other, quite restricted in scope. Liaison consonants almost always appear in a small number of contexts that can be defined in prosodic terms, optionally appear in an even smaller number of situations, and do not appear at all in many contexts where they might be expected prescriptively. Their appearance does break up vowel-vowel sequences in contexts that do not otherwise have hiatus, and non- standard liaison is often used to eliminate exceptions to this pattern.

In the case of vowel deletion, the data support a view of anti-hiatus deletion as both quantitatively and qualitatively different from vowel lenition motivated by other factors. Firstly, there is more deletion of vowels in hiatus than of vowels in other contexts. Secondly, where high

12 vowels undergo gradient lenition in non-hiatus contexts, the same pattern is not observed when they are in a vowel-vowel sequence. In hiatus contexts, high vowels simply do or do not delete in the same way as for non-high vowels, while in some other contexts devoicing is a step towards deletion.

For these reasons, a constraint that penalizes sequences containing two vowels in a row belongs in this analysis. The constraint *VV is operational in all three constraint levels, and can therefore be considered central to the system. Its effects are countered by a number of other constraints, or else no surface form would ever contain hiatus, but it is nonetheless necessary in an explanation of this aspect of QF phonology.

1.3.2 Feature-based analysis

In the grammatical model presented here, vowels are made the target of specific constraints via the features they bear. This relatively abstract view of phonology, wherein segments are made up of features, is in particular motivated by the coalescence and vowel deletion data. Most strikingly, in looking at which vowels delete most easily, there are clear groupings according to shared phonological characteristics. Moreover, the coalescence data are compatible with a view of coalescence as the combining of phonological features. These data are used as the basis for determining a set of features for QF vowels. The set of features that best fits the data is shown to follow the view that features are organized into a contrastive hierarchy (Dresher 2009).

1.3.3 Stochastic evaluation

I argue that the final level of constraint evaluation must be probabilistic in order to model the variability that is pervasive throughout the data. All of the anti-hiatus processes are variable in two ways: similar or near-identical phonological strings may on the one hand be realized in a number of different ways to avoid hiatus, and may on the other hand sometimes surface with hiatus and sometimes without. Also, some of the outcomes, such as glide insertion, are relatively rare. I argue that this situation, with rampant variation and rare variants, can best be captured through a powerful model, such as Stochastic Optimality Theory (Boersma & Hayes 2001). I consider a large number of constraint sets and rankings and settle on the one that best fits the data.

1.3.4 Multiple levels

A number of processes in the data interact in ways that lead to opacity. In particular, vowel deletion and liaison, and vowel deletion and dental stop assibilation, interact in ways that single- level OT is ill equipped for dealing with. Liaison consonants appear in the absence of triggering hiatus, and assibilated stops appear in the absence of triggering high front vowels. These facts suggest that vowel deletion occurs in some sense after liaison and assibilation. A straightforward way to model this is to have them take place later in a derivation. To do this, I adopt a multi- stratal model, with three serially linked constraint levels (Kiparsky 2010). These levels are defined prosodically, and correspond to the clitic group, phonological phrase and intonational phrase (Nespor & Vogel 1986, Hayes 1989), larger constituents than those in previous work. Based on this, one of the claims of this analysis is that different languages can define different prosodic domains as targets for constraint levels. Also, because different constraint levels are associated with different levels of variability, variation can be modeled without losing the ability to account for categorical patterns in the data.

1.4 Thesis organization

The thesis is organized into four main chapters. In the first two, all of the data are presented, along with methodological descriptions, and some preliminary analyses. The first of these two, Chapter 2, deals with cases where hiatus would not have been expected according to SF. The main situations where hiatus is not expected are following a fixed final consonant, with clitic- type words that sometimes have schwa, and in liaison context. Chapter 3 has to do with the situations where SF would lead us to expect hiatus. These contexts are mostly made up of regular sequences of two vowels, but also include some cases of h-aspiré items. In Chapter 4, the issues related to the variability within the data are explored. A set of features for analyzing the vowels of QF is also proposed. Chapter 5 begins with a discussion of the way the phonological processes in the data interact, and the resulting opacity. In the second half, all of the aspects of the proposal are put together. How the model works as a whole is shown for all of the types of data that make up the previous chapters. A short concluding chapter is also included.

Chapter 2 Hiatus Not Expected

There are a number of contexts where we would not expect to find hiatus, despite the fact that they involve a vowel-initial word. These contexts either trigger liaison, or involve the presence of a fixed final consonant or the absence of schwa, and are the subject of this chapter. In all of these situations, the rules of Standard French (SF) would lead us to expect that across the relevant word boundary a single vowel will be adjacent to a consonant, meaning that hiatus is not possible. As we will see, in some cases Québécois French (QF) does not depart dramatically from the basic assumption that these contexts will not involve hiatus, but there are pockets of data for which this is not true. Even though QF differs from SF in some crucial respects, the separation between contexts where hiatus is expected and those where it isn’t represents a basic split in the data.

This chapter is divided into three parts, corresponding to the three contexts and moving from the simplest to the most complex. In 2.1 I discuss vowel-initial words following a fixed final consonant, while in 2.2 I discuss the absence of schwa before vowel-initial words. Finally, in 2.3 I deal with liaison.

2.1 Fixed final consonants

When a vowel-initial word follows a word that is always pronounced with a final consonant, the outcome, rather predictably, tends to be as in 9 and 10.

9. Ça me stresse un peu [...] 5 [stల ϯsœ] (=/ stలϯs#œ/) ‘It stresses me out a little’ (I/6/36)

10. [...] juste avant que je parte [...] [Ћ௙sava] (=/ Ћ௙st#ava/) ‘just before I left’ (I/13/11)

5 The phonetic realization of r varies in QF, but I transcribe it as [ ల] everywhere.

In both cases, the first word would also end with a consonant if it were in isolation, or followed by a consonant-initial word. This is the reason for the term ‘fixed final consonant’ (Côté 2008 among others), which is used to distinguish these cases from those involving liaison consonants (see 2.3). Note however that in 10 the underlying consonant that is actually final is deleted in a common process of cluster simplification (Côté 2005b, Walker 1984). This doesn’t alter the classification of this and similar tokens as following a fixed final consonant in that what is essential is that the preceding word always ends in a consonant.

2.1.1 Methodology and results for typical cases

Tokens with fixed final consonants were identified in the first phase of extraction, when every instance of a vowel-initial word qualified as data (see 1.2.2.1). The following sections (2.3 and those in Chapter 3) rely heavily on a second phase of extraction where only tokens that were likely candidates for hiatus were identified. With a few exceptions (presented as they become relevant) aside, a likely candidate for hiatus was defined as a case where the target vowel-initial word followed a vowel-final word. Broadly speaking then, in the first stage any vowel-initial word was extracted, and in the second any vowel-initial word following a vowel-final word was extracted. As will become clear in this section and 2.2, the motivation for the first stage of extraction was to verify that the data did not present unexpected anomalies. The motivation for the second was that once this verification had been done, it did not seem useful to collect vast quantities of identical data.

Returning to the details of the extraction for the fixed final consonant tokens, a relatively large group of tokens consisting of items following il ‘he’ and ils ‘they’ was immediately excluded, because the final [ l] that appears in SF was always absent from the main speaker’s data. These tokens are instead dealt with in 2.3.6.3.1 and Chapter 3. This left some 94 tokens of a vowel- initial word following a fixed final consonant. 86 of these behave just as would be expected and as illustrated in 9 and 10. For these, hiatus is impossible because the first word’s final consonant cannot delete (unless it is preceded by another consonant) nor can vowel epenthesis take place, 6

6 This is in contrast to some varieties of European French that tolerate the insertion of schwa in a vast range of phonological contexts, particularly in formal speech.

16 meaning that there will never be a vowel-vowel sequence. Other than the two groups of exceptions discussed below (2.1.2.1 and 2.1.3), no further tokens of this kind were extracted in the second phase, as there is sufficient data to show that expectations that this group does not have hiatus are met.

2.1.2 Vowel deletion following a fixed final consonant

In eight cases, the data are not as would have been expected. For four of these, the initial vowel of the second word deletes. While this does not change the fact that hiatus is not possible here, the tokens may be relevant to the analysis of vowel deletion presented in subsequent chapters. In two cases, the vowel-initial word is excuse ‘excuse’ pronounced [ skyz] (rather than [ ϯkskyz]) in the construction excuse-moi ‘excuse me’. This is a very common pronunciation and may be best analyzed as fixed (involving allomorphy) rather than the result of active phonological processes. The third case of initial vowel deletion is ici ‘here’ being pronounced [ si] instead of [ isi]. Such cases are discussed in Chapter 3.

2.1.2.1 Avec

The last of the four cases of initial vowel deletion is with avec ‘with’. The initial stage of extraction revealed that this lexical item seemed much more subject to deletion than other words. In order to collect as many tokens as possible to help in testing this, all instances of avec were extracted in the subsequent stages of data collection, even where it followed a fixed final consonant. This yielded a total of 68 cases for the main speaker, which did turn out to show dramatic deletion patterns. From a quantitative perspective, the rate of deletion of the initial vowel in avec is much higher than for other words, as shown in Table 2.

Table 2. Initial vowel deletion for avec ‘with’ 7 After a fixed consonant After a vowel Other contexts Deletion (N) 8 19 No deletion (N) 22 15 3 8 Unclear (N) 1 Deletion Rate 27% 56% 0%

The three central rows of Table 2 give the number of tokens, according to whether the first vowel of avec deletes and to whether the word preceding avec ends with a consonant or a vowel. The bottom row gives the percentage of deletion of the first vowel of avec according to previous context, with 27% deletion after a fixed final consonant and 56% after a vowel. We have yet to establish a baseline for deletion rates but for the time being it is sufficient to recognize that the first vowel of avec deletes much more frequently than the average word. Just as the deletion rate of 27% following a consonant marks avec as different from other words (with the possible exception of excuse ‘excuse’ and ici ‘here’ discussed above), so too does the 56% deletion rate following a vowel differ significantly from the average for all lexical items combined (see Figure 9 and Figure 10 in Chapter 3).

Perhaps even more dramatic than the quantitative differences in deletion patterns is the fact that avec deletes in qualitatively different ways than other words. It isn’t only the first segment that can be the target deletion and there are myriad possible outcomes corresponding to avec , as 11- 17 illustrate.

11. [...] pis elle est en amour avec Greg. [amuల avϯk] (=/ amuల#avϯk/) ‘and she’s in love with Greg’ (I/21/10)

12. Je suis pas comme ça avec eux [...] [sϪ vϯk] (=/ sϪ#avϯk/) ‘I’m not like that with them’ (I/4/123)

7 This includes liaison (N=1; see 2.3) and no schwa (N=2; see 2.2).

8 In this case, the preceding vowel is also [ a] and while one of the two vowels deletes, it is impossible to determine which one. This token is excluded from the deletion rate calculation.

13. [...] il y a rien avec elle [...] [లjϯ Єϯk] (=/ లjϯ#avϯk/) ‘nothing’s going on with her’ (I/33/17)

14. [...] tu détournes le regard avec un petit sourire [...] [లœցϪల a⍝ϯk] (=/ లœցϪల#avϯk/) ‘you give a sideways glance with a little smile’ (I/1/9)

15. [...] aux femmes enceintes, avec les bébés [...] [asϯt ϯk] (=/ asϯt#avϯk/) ‘to pregnant women, with babies’ (I/28/36)

16. Je parlais de ça avec Fred tantôt [...] [sϪ ϯjk] (=/ sϪ#avϯk/) ‘I was talking about that with Fred before’ (I/33/13)

17. Je vas y aller avec toi. [ale k] (=/ ale#avϯk/) ‘I’m going with you.’ (I/17/15)

There is something of a deletion progression from 11 through 17. In the first case we get every segment of the underlying form. In the example in 12, only the first vowel of avec deletes; in 13 this deletion takes place too, and the consonant that follows lenites. In 14 the [ a] survives but the following [ v] deletes completely, while in 15 both these segments are absent. In 16 it seems that the first two segments also delete, and then the remaining vowel diphthongizes, although alternative analyses are conceivable. This example, which represents an infrequent pattern, is also of interest because despite all the deletion, the resulting sequence contains a hiatus. Finally in 17, all that remains of avec is the last segment. The diversity of possible pronunciations is important: we might have expected to see just two kinds of forms, full and reduced, in a way that might have fit with an analysis through allomorphy, but this is not at all what is in the data. 9

Clearly, there are forces that have nothing to do with hiatus avoidance causing deletion within avec . At the same time, the fact that the deletion rate in Table 2 is twice as high after a vowel as after a consonant does suggest that vowel-vowel sequences are being avoided. This gives a first

9 It is conceivable that there are two different underlying forms for avec (one full (/ avϯk/) and one reduced (e.g. / ϯk/)) rather than a single one as given in the examples above.

19 taste of what will be a recurring tension in subsequent sections. On the one hand, segments seem to simply delete in a way that can’t be attributed to pressures as structured as hiatus avoidance. This is perhaps unsurprising given that the data are drawn from rapid, informal speech, although the extent of the deletion is quite dramatic. On the other hand, constraints on well-formedness do seem to come into play, but often in a way that is most visible when tendencies in the data are examined rather than individual tokens. The tokens of avec following a vowel-final word will be further discussed in 3.1, with the conclusions drawn there also applying to the cases after fixed final consonants discussed here.

2.1.3 Not always a fixed final consonant: elle

The final four cases that don’t fit the general pattern for fixed final consonants all involve a vowel-initial word following elle ‘she’. For some tokens such as in 18, elle (which in this variety always begins with the low vowel [ a]) behaves just like any other word with a fixed final consonant.

18. Qu’est-ce qu’ elle a répondu? [alϪ] (=/ al#Ϫ/) ‘What did she answer?’ (I/50/19)

However, in a pattern that has been the subject of detailed studies (Poplack & Walker 1986, Bougaïeff & Cardinal 1980, among many others), in many cases the final [ l] in elle fails to appear, as in 19.

19. [...] mon dieu, elle est rock and roll cette fille-là [...] [ϯ] (=/al#e/) ‘my god, she’s ‘rock and roll’ that girl’. (I/4/57)

Because of the existence of such tokens, words following elle were extracted throughout the data set in the same way as avec . The tokens where elle surfaces with a final consonant behave just like normal words with fixed final consonants: hiatus is not possible, and no deletion takes place. However, the deletion of the final [ l] means that hiatus would indeed be possible and might in fact be expected. For this reason, this group of tokens belongs in Chapter 3 and will be dealt with there.

2.2 No schwa

The second situation where we would not expect to find hiatus despite the presence of vowel- initial words is with the clitics ce , de , je , le , me , que , se and te . These sometimes surface with a schwa but would not be expected to before a vowel. 20 illustrates this with the first person singular pronominal clitic je , which appears without schwa before the vowel-initial verb, but with it before the consonant-initial verb that follows it. QF schwa is pronounced as front, rounded and lax (Côté 2005b) and so is transcribed as [ œ].

20. [...] quand j’accepte quelque chose, je reviens pas là-dessus, là. [Ћ aksϯp] [Ћœ లvjϯ] ‘when I accept something, I don’t go back on it’ (I/2/40; n.t.)

Whereas the presence of schwa after a fixed final consonant (see 2.1 above) is a feature of some prestige varieties of French, having a schwa appear before the vowel-initial verb would be completely ungrammatical (*[ Ћœaksϯp]). As for pre-consonantal contexts, the situation is quite complex, and the subject of much research. 10 For our purposes, it is sufficient to note that in QF schwa tends not to appear across word boundaries if the resulting sequence is of only two consonants, and that a number of longer consonant sequences are also tolerated in ways that vary according to formality and rate of speech (Picard 1991).

2.2.1 General pattern

As described above, we would not expect to find schwa before vowels, and the data bear this out. In the first stage of extraction, 153 tokens of vowel-initial words following a clitic were identified. For 134 of these the situation is just as would be expected: the vowel-initial item appears following the clitic consonant and no schwa surfaces (as for the three clitics in 21).

10 Beginning with Grammont’s (1914) early observation that schwa appears to break up sequences of three or more consonants (the loi des trois consonnes ), followed by numerous refinements to this generalization based on sonority and phonotactics (see Côté 2000 and references therein).

21. [...] tu sais, ce qu’elle m’a faite là, l’autre fois là [...] [ka] [ mϪ] [lot] ‘you know, what she did to me, the other time’ (I/5/5; 6; 7)

To date, I have not given underlying forms for examples involving schwa, given that it is a contentious issue whether to treat schwa as underlying or epenthetic in these situations (see 1.1).

For these contexts involving clitics, I will adopt the position that schwa is not underlying but instead surfaces through epenthesis, following Côté & Morrison (2007). Côté and Morrison present a number of arguments against the more traditional view that schwas are present in underlying representations. Firstly, unlike schwas within words, schwas at clitic boundaries are not lexically contrastive. The authors provide the examples repeated in 22 below to illustrate the contrastive status of word-internal schwas (Côté & Morrison 2007, 164: formatting and transcriptions modified).

22. pelouse / pœluz/ blouse / bluz/ place / plas/ [pluz]~[ pœluz] [bluz] *[ bœluz] [plas] *[ pœlas] ‘lawn’ ‘blouse’ ‘place’

But Côté and Morrison point out that no such contrast holds for schwas within clitics, citing the fact that there isn’t for instance a clitic that can be pronounced [ t] or [ tœ] and another that can only surface as [ t]. Côté and Morrison also refute other arguments for analyzing clitic schwas as underlying. They show that the parallels between their distribution and that of word-internal schwas can be attributed to prosodic factors rather than their underlying status, and find that previously reported residual lip rounding where clitic schwas do not surface is not the result of schwa deletion. In the absence of evidence for viewing schwas within clitics as underlying, the epenthetic analysis is simpler given the absence of lexical contrast.

This view also allows for a simpler explanation of the previously discussed generalization that schwa appears to break up impossible consonant clusters. For instance in 23, schwa appears in half of the places it could.

23. Mais ce que je me suis rendu compte [...] [skœЋmœ] ‘But what I’ve realized’ (I/16/n.t.)

This sequence could have been realized in other ways ([ skœЋœm] or [ sœkЋmœ] for instance, although the attested form sounds most natural), but interestingly the presence of every possible schwa does not at all sound natural for QF (??[ sœkœЋœmœ]). If all the schwas were posited to be underlying, it would of course be possible to motivate their deletion, but the analysis becomes unwieldy in particular because it must make a distinction between sequences that are acceptable word-internally and those that are acceptable across word boundaries. 11 If we adopt the view that some schwas (word-internal ones) are underlying, but that these are not, the analysis is simpler.

This view is also bolstered by the fact we have evidence of schwa epenthesis at word boundaries in cases like 24 in the data.

24. Fait que j’ai pas faite de crise, rien , mais [...] [kలizœలjϯ] (=/ kలiz#లjϯ/) ‘So I didn’t have a fit, or anything, but’ (I/2/n.t.)

This is a very common site for schwa epenthesis given that most Crj clusters are bad in QF.

The schwa data presented so far may give the impression that a mechanical application of a constraint limiting possible consonant sequences would accurately predict all of the data. This is far from the truth given the vast number of factors (some of which I mention above) that contribute to the likelihood of schwa surfacing. To give a taste of this, 25 shows two cases where we might not expect schwa to surface but it does.

25. [...] je pense les deux on avait envie de parler, de s’exprimer [...] [avidœpaలledœsϯkspలime] ‘I think both of us felt like talking, like expressing ourselves’ (I/3/n.t.)

In both instances of de , the absence of schwa would have led to groups of just two consonants, which would have been fine. A partial explanation for 25 is that the host of the show was interviewing the speaker, a context that is likely to encourage more formal, slower speech.

11 For instance squelette ‘skeleton’ is always pronounced with a schwa ([ skœlϯt]) (Côté &

Morrison 2007: 161), although the sequence [ skl] would be licit if the two first consonants were clitics as in ce que les [...] ‘what the’.

If we adopt the view that schwa is epenthetic at word boundaries, these no schwa contexts are actually basically the same as the fixed final consonant contexts described in 2.1, as they both involve a vowel-initial word following a consonant in the underlying form. Similarly to fixed final consonant tokens, the second stage of extraction in general did not target words that followed a clitic (see 2.2.3 for one type of exception).

2.2.2 Vowel deletion following a clitic

The 19 cases that were not completely as expected in the first stage of extraction did not involve the presence of schwa, but rather the deletion of the vowel from the vowel-initial words. They involve five cases of the vowel of est ‘is (3p.s.)’ not surfacing (as in 26), 12 cases of the first vowel of était ‘was (3p.s.)’ not surfacing (as in 27), and a further two of the deletion of the first vowel of étais ‘was (1p.s.)’ (as in 28).

26. [...] c’est encore pire [...] [stakѐల] (=/ s#e#akѐల/) ‘it’s even worse’ (I/2/21)

27. [...] c’était les plus beaux là, après là. [stϯ] (=/ s#ϯtϯ/ or / s#etϯ/) ‘they were the best looking, after that’ (I/4/27)

28. [...] excuse-moi, j’étais juste mal à l’aise [...] [Ѐtϯ] (=/ Ћ#ϯtϯ/ or / Ћ#etϯ/) ‘forgive me, I was just uncomfortable’ (I/6/18)

In 26 and the other four tokens like it, all that remains of est is the liaison consonant [ t], which wouldn’t have surfaced if not for the fact that the word that follows is also vowel-initial. The cases like 27 and 28 are less complicated: the vowel deletes and in the latter case this deletion means that the clitic is adjacent to a voiceless stop, causing it to devoice.

Having seen similar cases in 2.1.2, it is perhaps not surprising to see vowels delete between two consonants. However what is dramatic here is the extent to which the behaviour of est , était and étais is categorical in the main speaker’s data. It is worth keeping in mind that this is based on relatively few tokens, since cases like these were not targeted in the second phase of extraction. But if they had been, it is likely that many more tokens with est , était and étais would have been found. The relevant constructions are not only quite frequent, but the deletion of the initial vowel sounds very natural in QF.

For est , there are no cases where deletion does not take place in the same context as the five cases with deletion. While there are 32 tokens where the vowel does not delete following one of the relevant clitics, in every instance the following word is consonant initial, as in 29.

29. Mais je veux pas en parler, parce que c’est personnel. [se] (=/ s#e/) ‘But I don’t want to talk about it, because it’s personal.’ (I/5/40)

Every time the word after est is vowel-initial we get vowel deletion, either as in 26 or as in 30, a similar construction in which no liaison consonant appears either. 12

30. [...] c’est à force de les connaître [...] [sa] (=/ s#e#a/) ‘it’s once you get to know them’ (I/4/25)

In the case of était and étais every single token following the clitics ce and je respectively have deletion of the first vowel. This is not at all the case with other clitics that don’t interact with schwa. For instance, with il (3 p.s.) and ils (3 p.p.), the vowel in étai(en)t never deletes as illustrated in 31.

31. Mais là il était vivant quand t’as faite ça, là? [jetϯ] ‘But it was alive when you did that?’ (I/42/18)

The motivation for the vowel deletion will be discussed further in Chapter 3 and I discuss the cases that involve liaison consonants (like 26) in section 5.2.2. An additional observation is that the clusters that result from the deletion ([ st] and [Ѐt]) are pretty much the optimal two-consonant sequences from the perspective of QF phonotactics.

2.2.3 Schwa instead of no schwa

Within the second stage of extraction, a handful of tokens that did not fit with the data for these clitics were found. In all these cases (listed below), a schwa was inserted before a vowel-initial

12 Because est deletes completely in these cases, they are not factored into the numbers for vowel deletion given above. Assigning token status to null elements seemed overly reconstructive, especially given the existence of morphologically conditioned deletion in QF.

25 word and for all but 34, these are truly dramatic exceptions to what would have been predicted since hiatus is created.

32. [...] c’est difficile à savoir ce que eux pensent de leur côté. [kœø] ‘it’s hard to know what they’re thinking over on their side’ (I/37/15)

33. [...] parce que eux ont vu le- le show [...] [kœø] ‘because they saw the- the show’ (I/50/23)

34. [...] c’est un an que ils ont le droit de demander n’importe quoi? [kœjѐ] ‘it’s a year that they’re allowed to ask anything?’ (I/7/18)

35. Là elle a conté un autre affaire, que elle voulait épater [...] [kœa] ‘Then she told us another thing, that she wanted to wow’ (I/27/42)

The last two examples should most likely be put aside because in both cases the recording indicates that the speaker is hesitating about how to continue the utterance. This is most clear in 35, where there is a 0.69 sec pause and an audible breath intake between que and elle . In 34, the schwa is simply drawn out slightly, but in a way that indicates hesitation. The first two examples require a real explanation. They both involve the pronoun eux following schwa. This is not the usual masculine third person plural subject pronoun ils , but instead the tonic pronoun (also referred to as ‘stressed pronoun’). In QF, these are frequently used before a nominal pronoun as resumptive pronouns, often to mark topic or focus (e.g. Eux, ils vont venir. ‘Them, they’ll come.’). Here they are used alone, and definitely seem to be used to mark focus. One possible explanation for the presence of schwa could be that focused items align with a syllable boundary. If this were the case, schwa insertion with que ‘that’ would be reasonable in both cases, since without it the previous syllable would have a three consonant coda.

2.3 Liaison

Liaison, which involves the presence of an otherwise unpronounced consonant between words (as in 36; cf. 37), has been amply described in the literature (see Chevrot et al. 2005 and references therein, and 1.1).

36. les enfants [lezafa] ‘the children’ (I/32/1)

37. les familles [jگlefam] ‘the families’ (I/23/n.t.)

Liaison is an exceedingly complex phenomenon. Whether or not an intervening liaison consonant surfaces has been shown to be sensitive to a range of morphological, syntactic, prosodic and lexical factors, and under a strong influence of register and style. While it is possible that not all effects apply to all speakers, a complete analysis of liaison would require accounting for all these factors. However, despite having a substantial number of tokens for liaison, the data are not nearly sufficient to come to strong conclusions for some of the subtler tendencies. This section will present a description and preliminary analysis of the liaison data, but there is no pretence of completeness.

2.3.1 Liaison context

Liaison is widely recognized to be variable, as the traditional distinction between obligatory, optional and prohibited liaison makes clear. I coded the data following this classification, as summarized in Table 3.13 The category of prohibited liaison is made up of tokens following a word that could license liaison in other contexts.

13 I strayed from traditional classification in not including contexts that would allow for liaison with r (N=31) and p (N=1). This type of liaison does not occur in informal QF and its inclusion might have been misleading. See 2.3.6.5 for further discussion of liaison consonant identity.

Table 3. Coding for liaison context determinant + noun adjective + noun OBLIGATORY pronoun + verb (and vice versa) LIAISON numeral + verb peut-être quelques uns temps en temps auxiliary + verb OPTIONAL verb + verbal complement LIAISON adverb + adjective preposition + noun plural noun + adjective PROHIBITED across major prosodic boundaries LIAISON singular noun + anything

The resulting numbers of tokens are shown in Figure 1 below.

Figure 1. Liaison results according to liaison context

300 250 200 150 100 liaison 50 no liaison 0 Obligatory liaison Optional liaison Prohibited liaison

These traditional classifications are quite successful in separating the contexts where we would expect liaison from those where we wouldn’t. There are no cases of liaison in prohibited contexts, whereas the great majority of obligatory contexts have liaison. The optional liaison context is the only one showing robust variation. A discussion of this variation (2.3.4) is preceded by remarks on obligatory liaison (2.3.2), and other cases of liaison (2.3.3).

2.3.2 Obligatory liaison

Contexts such as between a determiner and a noun (as in 38) and between a pronominal subject and a verb (as in 39) appear frequently and overwhelmingly have liaison.

38. C’est un ami ? [œnami] ‘He’s a friend?’ (I/46/14)

39. On est prêtes. [ѐne] ‘We’re ready.’ (I/20/1)

Some cases such as 40 (N = 6) were also considered to have a liaison consonant despite the fact that the feminine noun would in SF require the fixed final consonant determinant une .

40. [...] je montais pour faire un(e) activité [...] [œnaksi"vi"te] ‘I would go up to do an activity’ (I/12/35)

Variation in gender has been documented in QF (Klapka 2002); the situation may be compounded by the pre-vocalic context, where there may be a tendency to neutralize gender differences.

While 24 instances of non-liaison in this context may seem like a large number, most of these involve the pronoun ils followed by a verb (N=17). In all cases, the pronoun surfaces as a glide as in 41.

41. Ils étaient deux filles pis lui. [jϯtϯ] ‘They were two girls and him.’ (I/2/36)

In this QF variety, the pronoun ils does not license the presence of the z liaison consonant that SF deems obligatory (see 2.3.6.3.1 for more discussion of il/ils ).

Of the remaining seven cases of non-liaison in an obligatory liaison context, four involve the deletion of V2, as in 42.

42. On allait avec ses chums. [ѐlϯ] ‘We went with his friends.’ (I/12/39)

The absence of a liaison consonant here may seem less surprising, but see discussion of similar cases in 2.3.5. A further case of non-liaison is likely due to the h-aspiré status of the lexical item involved and will be discussed in 3.2. It is completely unsurprising that an h-aspiré item would block liaison, as this is probably the most salient characteristic of such words. However, it is included here because it fits with the way the variable context has been defined in that it is

29 phonetically vowel initial. Putting aside these cases, this leaves a mere two cases (43 and 44) where liaison would be expected but does not occur.

43. C’est rare qu’ on est arrêtées sur vos photos. [ѐ⍝e] ‘It’s rare that we are stopped on your pictures.’ (I/46/1)

44. Après ça, nous-autres on y va. [ѐjvϪ] ‘After that, we go there.’ (I/27/64)

2.3.3 Other liaison

Because contexts that are not traditionally thought of as allowing liaison are not included, some tokens (N = 8) are not yet represented in these figures. These fall into two categories: the first involves the insertion of l following ça as in 45, and the second the insertion of n preceding a nasal vowel, most often en , as in 46.

45. Tu laisses tomber dans l’eau, tu mets un poisson mort, mais là, ça ouvre . [saluv] ‘You drop it in the water, you put a dead fish, but then, it opens.’ (I/42/30)

46. Celui-là, on va en partager, tiens. [vϪna] ‘This one, we’ll share some, here.’ (I/39/7)

2.3.4 Variation in liaison

While the optional liaison context clearly has variability, with about 1/5 of contexts having liaison and 4/5 not, it is far from homogeneous. Table 4 shows the extent to which the variation is lexically determined.

Table 4. Breakdown of optional liaison tokens according to first word LIAISON POSSIBLE (liaison N / total N)

liaison C = n bien (4/4), en (11/12) 14 , rien (1/2) liaison C = t es (4/4), est (27/51), sont 15 (0/1), suis (11/14), tout (1/1) liaison C = z dans (3/5), très (1/1)

NO LIAISON (total N)

any verb but être (100) étais (3), était (4), étaient (3), soit (1) devant (1), directement (1), pendant (1), quand (6), sûrement (1), tellement (1), vraiment (2) desfois (2), mais (38), mieux (1), moins (3), pas (46)

Clearly the only real pockets of variability involve present tense forms of the verb être ‘to be’ (all of which take t as liaison consonant, unlike SF) as the difference between 47 and 48 illustrates, and to a far lesser extent dans ‘in’.

47. Mais on est encabannés. [e takabane] ‘But we’re shut in.’ (I/7/14)

48. On est entourés de caméras. [eature] ‘We’re surrounded by cameras’. (I/16/44)

Liaison never occurs with any verb other than être , either before its complements, or between an auxiliary and a verb. Notably, avoir is never followed by liaison, as 49 and 50 illustrate.

49. [...] sur toute les familles, il y en avait une qui avait pas d’enfants. [avϯ௙n] ‘out of all the families, there was one that had no children’ (I/23/73)

50. [...] j’ai des amis qui m’ ont amenée de force, merde. [ѐamne] ‘I have friends who forced me to come, damn it’ (I/11/14)

14 The non-liaison token is an h-aspiré word: see 3.2 for discussion.

15 sont is included in the possible liaison category because M, whose pattern of liaison appears to be identical to I’s, has liaison with the single token of sont in her data.

2.3.5 Liaison as anti-hiatus repair

The liaison cases presented thus far, and in particular those such as 51, may give a sense that liaison does indeed serve to break up hiatus.

51. [...] il va en faire des affaires. [vϪna] ‘he’s going to do (some) things’ (I/28/26)

But there are a number of cases that cast doubt on this. Many instances of liaison (N=56) are accompanied by deletion of V1 or V2, leading the liaison consonant to be either consonant adjacent (as in 52 and 53) or even utterance initial (as in 54).

52. C’est un gars de bar là. [stœ] (=/ s#e#œ/) ‘He’s a bar guy.’ (I/13/21)

53. De pourquoi t’es ici [...] [tetsi] (=/ t#e#isi/) ‘why you’re here’ (I/17/7)

54. Je suis en train de [...] [ta] (=/( Ћ)# sіi#a/) ‘I am (progressive)’ (I/41/67)

These forms may appear to support the view that liaison is not motivated by hiatus avoidance (as Morin 2005 would likely have it). However, the fact that such a motivation is not apparent at the surface level does not mean that it does not come into play at any level of the grammar. In a huge majority of the data, liaison consonants appear in sequences that have underlying VV. Indeed, there are only three instances, in 55 to 57, where this is not the case.

55. Parles-en . 16 [paలlza] ‘Talk about it.’ (I/19/11)

16 No underlying forms are given here, pending further discussion: these are in 60, 61 and 62.

56. Pis l’Halloween, bien je la passais, là, jusqu’aux petites heures là [...] [tzœіలگpts] ‘And Halloween, I’d go door to door until late’ (I/23/9)

57. [...] quelques uns des chums que j’ai eus [...] [kϯlkœzœ] ‘some of the boyfriends’ (I/4/19)

However, most liaison does not appear in contexts like those above, but instead in contexts with underlying hiatus sequences. This means that, putting aside these three cases, an alternative explanation could be that liaison is indeed motivated by hiatus avoidance, but that vowel deletion subsequently hides this motivation. In this view, the resulting forms (such as those in 52, 53 and 54 above) involve opacity, in the sense that in a rule-based analysis liaison would be an opaque rule. As we will see, the issue of opacity is indeed a central one for the analysis here: it is discussed in Chapter 5, with 5.2.2 addressing the liaison data in particular.

2.3.6 The beginnings of an analysis

In the previous sections, we looked at the distinction between optional and obligatory liaison contexts, and examined the variability of liaison and whether or not it could be attributed to hiatus avoidance. What emerges from these data is a portrait of liaison as on the one hand more restricted than analyses of other varieties suggest, and on the other more productive than many approaches assume. It is very possible that other varieties of French show qualitatively different patterns that would require a different analysis, but I limit myself here to attempting to account for these speakers’ grammars.

Liaison is restricted in that a small number of contexts account for the bulk of the data, and that the choice of liaison consonant seems to be more predictable than not. Liaison with n appears in all but seven cases following a nasal vowel, with the remaining cases preceding a nasal vowel. Of 59 cases of liaison with t, 43 follow a present tense form of être ‘to be’ and a further 12 occur within peut-être ‘maybe’. Liaison with l occurs following ça ‘it’. Of 69 cases of z liaison, 42 precede plural nouns and 20 intervene between a subject or object pronominal clitic ( les , nous , vous ) and a verb.

Liaison is productive as evidenced in part by the non-standard epenthesis of n (as in 51) and l (as in 45), which serves to fill in the gaps where SF would disallow liaison. As I discuss in 2.3.6.2

33 and 2.3.6.3, certain contexts (defined prosodically and/or syntactically) essentially require liaison.

2.3.6.1 Nature of the liaison consonant

This productivity and relative predictability makes the approach suggested by Côté (2005a), in which some liaison consonants are underlying and others are not, the best fit for the data. The main benefit of the proposal is that it treats most instances of liaison consonants as epenthetic, unlike the majority of analyses, both recent (Bybee 2001, Tranel 2000, and others) and not (Selkirk 1974, Schane 1968, and others). Because a straightforward generalization seems able to predict which liaison consonant appears in the majority of the data here, there is no reason to argue that liaison consonants are underlying. This has the advantage of making unnecessary the rather complex mechanisms, floating consonants (Encrevé 1988, among others) for instance, required to ensure that an underlying consonant appear only in pre-vocalic environments.

Analyzing liaison consonants as absent from the underlying form of the first word in the two- word sequence may seem unorthodox, given how longstanding the traditional analysis is, as well as the fact that it matches French orthography. However, Côté provides a number of strong empirical arguments for abandoning the view that liaison consonants are present at the end of the relevant words. One line of evidence comes from acquisition data. There are many different kinds of attested child errors involving liaison: using the wrong liaison consonant ([ œzurs] for

[œnurs] un ours ‘a bear’), having a liaison consonant in a non-liaison context ([ papanurs] for

[papaurs] papa ours ‘papa bear’), not having a liaison consonant in a liaison context ([ œurs] for

[œnurs] un ours ‘a bear’), using the wrong word-initial consonant ([ œnϯbr] for [ œzϯbr] un zèbre

‘a zebra’), and omitting a word-initial consonant ([ ցroϯbr] for [ ցrozϯbr] gros zèbre ‘big zebra’) (all examples from Côté 2005a: 83). But some types of errors that would be expected if children were learning to analyze liaison consonants as part of the first word are unattested: children do not omit fixed final consonants (for instance *[ ѐnϯ] for [ ѐnϯt] honnête ‘honest’), nor do they have liaison consonants before consonant-initial words (for instance *[ ցrozbyro] for [ ցrobyro] gros bureau ‘big desk’) (Côté 2005a: 84). The contrast between the attested errors and the unattested ones suggests that the path of acquisition is not leading to having liaison consonants be a part of the underlying form of the first word.

Another source of evidence against underlying liaison consonants at the ends of words comes from the fact that they can appear following a pause, as in 58 and 59.

58. un robuste, mais petit, enfant [pœti (pause) tafa] ‘a robust, but small, child’ (Côté 2005a: 85; from Morin 2003)

59. j’en ai un, ami [œ (pause) nami] ‘I have one, (a) friend’ (Côté 2005a: 85; from Tranel 1990)

The liaison consonants would be expected to appear before the pauses if they belonged to the first word. Finally, Côté cites evidence that liaison consonants are acoustically and perceptually different from both fixed final consonants and fixed initial consonants. This fact offers empirical support for analyzing liaison consonants as being represented differently from other French consonants. Côté’s analysis allows for an explanation of the fact that liaison consonants do not behave as though they are the final underlying segment in some words.

One of the most striking aspects of Côté’s proposal is that liaison is analyzed as a series of heterogeneous processes: while most liaison consonants are epenthetic, some are underlying, attached either to the first word or the second. Two types of consonants for which underlying status is proposed will be crucial here. The first involves the plural marker / z/ which, following Morin & Kaye (1982), Côté suggests could be treated as a prefix. This approach would be used to deal with sequences made up of a plural determiner or adjective followed by a plural noun. In the data here, there is a wide variety of lexical items that enter into this configuration, and all of them have liaison. Indeed, plural determiners followed by the liaison consonant [ z] include aux , des , les , mes , nos , ses and tes , while plural nouns preceded by [ z] are of course even more numerous. Given this, it seems reasonable that faced with this type of data, a learner will organize her representations in such a way that / z/ comes to represent number rather than storing a large number of representations ending or beginning with / z/ as tends to be assumed.

The second type of liaison consonant proposed to be underlying has to do with the surfacing of a consonant between a verb and a clitic, such as [ z] in vas-y ‘go (there)’ and [ t] in va-t-il ‘will he’.

Following Morin (1979a, b), these are treated through allomorphy, with for instance / zi/ and

/ti(l)/ for the cases above. The allomorphs with / z/ are associated with the imperative mood, and those with / t/ with interrogatives and other structures with subject-verb inversion.

Adopting these proposals means that the few cases where the surfacing of a liaison consonant could not be attributed to hiatus avoidance (55 through 57, repeated here as 60 through 62) are dealt with as exceptional instances of underlying consonants.

60. Parles-en . [paలlza] (=/ paలl#za/) ‘Talk about it.’ (I/19/11)

61. Pis l’Halloween, bien je la passais, là, jusqu’aux petites heures là [...] (/tzœіల] (=/ pœtit#z+œలگpts] ‘And Halloween, I’d go door to door until late’ (I/23/9)

62. [...] quelques uns des chums que j’ai eus [...] [kϯlkœzœ] (=/ kϯlk#z+œ/) ‘some of the boyfriends’ (I/4/19)

Indeed, for 60 /za/ would be the underlying form of the pronoun, and for 61 and 62 an underlying prefix / z/ would be present (see Tableau 1 and Tableau 2). This means that in all other cases, mostly corresponding in Côté’s proposal to the default case of epenthesis, a liaison consonant surfaces to break up an underlying VV sequence.

2.3.6.2 Constraints for categorical underlying liaison

Adopting a standard Optimality Theory framework (Prince & Smolensky 1993/2004) for the time being, a high ranking *VV (No hiatus) constraint dominating DEP (No epenthesis), seems to best explain the facts. As discussed in section 2.3.5 numerous cases of vowel deletion serve to make the effect of *VV opaque. This opacity is discussed in section 5.2.2, but for the time being, I will assume that liaison occurs on a derivationally earlier level than vowel deletion, indicated as Stratum 1 in the tableaux in this section. Following Anttila et al. (2008), no deletion is possible on this early level and so an undominated constraint MAX (No deletion) dominates *VV. Tableau 1 and Tableau 2 show the situation for the cases with underlying consonants discussed above. The selected candidate will get fed into further strata, so in Tableau 2, the winner is not the surface form. It is worth noting that as Côté does not formalize her proposal, it is not clear to what extent I am straying from the implementation she intends.

Tableau 1. Parles-en parles-en ‘talk about it’ Stratum 1 17 /paలl#za / MAX [*VV] DEP a. paలlza b. paలla *!

Tableau 2. Quelques uns quelques uns ‘some’ Stratum 1 /kϯlk#z+œ/ MAX [*VV] DEP a. kϯlkzœ b. kϯlkœ *!

Another type of exceptional situation discussed by Côté involves prenominal masculine adjectives. There are a handful of them in these data, and all have liaison. Côté treats these as the result of allomorphy as well, following Morin (1992). A major motivation for this has to do with the fact that some of these retain the word-final nasal vowel, as in 63, while in other cases the vowel before the liaison consonant denasalizes, as in 64 and 65.

63. aucun approchement cf. aucun [ okœ] ‘no’ [kœnapలѐЀma] ‘no closeness’ (I/4/85)

64. plein air cf. plein [ plϯ] ‘full (masc.)’, pleine [ plϯn] ‘full (fem.)’ [plϯnϯjల] ‘outdoors’ (I/34/9)

65. ancien amant cf. ancien [ asjϯ] ‘former (masc.)’, ancienne [asjϯn] ‘former (fem.)’ [asjϯnama] ‘former lover’ (I/12/2)

It is difficult and unwieldy to provide a phonological account of which vowels denasalize and which do not, which is why the allomorphy solution seems best. Moreover, the forms that surface without a nasal vowel here are the feminine of the adjectives in the same way as for alternations that could not be purely phonological. For instance, in citation form, ‘new’ is

17 I assume the other allomorph / a/ is ruled out either a priori or through an undominated syntactically-defined constraint.

37 nouveau [nuvo] in the masculine and nouvelle [nuvϯl] in the feminine. Before a vowel-initial word however, the masculine also surfaces as [ nuvϯl] (i.e. in nouvel amant ‘new lover (masc.)’). Such forms seem to require allomorphy as an explanation, and the forms related to liaison could fit that pattern. Where the nasal vowel is preserved, the additional allomorph would not be the feminine but instead a form identical to the masculine but with a consonant at the end. Tableau 3 shows how this approach would work for an example with denasalization (65 from above).

Tableau 3. Ancien amant ancien amant ‘former lover’ Stratum 1 /{ asjϯ, asjϯn}# ama/ MAX [*VV] DEP a. asjϯn ama b. asjϯ ama *!

This tableau shows allomorphy as resulting from a choice between two underlying allomorphs, with non-optimal possibilities being ruled out through constraint ranking. This simplified view seems sufficient for our purposes, but see Kager (1999: 413-20) and references therein for a discussion of allomorphy and OT.

2.3.6.3 Constraints for categorical epenthetic liaison

With the exception of the forms temps en temps ‘sometimes’ and peut-être ‘maybe’, which are presumably single words in the lexicon with an underlying consonant, the only unaccounted cases left that obligatorily have liaison involve a clitic. The constituents created by cliticization can preliminarily be thought of as phonological words (Selkirk 1974): this variety does not tolerate the violation of *VV within phonological words, and employs liaison in a productive fashion to avoid it. The contrast between optional and obligatory liaison comes from the fact that *VV is ranked lower for larger prosodic constituents. This effect will be captured by ranking

[*VV] PWd (No hiatus within a Phonological Word) above [*VV] PPh (No hiatus within a Phonological Phrase). Tableau 4 shows the effect of these constraints for a determiner followed by a noun, bearing in mind that plural sequences will be analyzed with the underlying prefix discussed above.

Tableau 4. Un étang un étang ‘a pond’ Stratum 1 /œ#eta/ MAX [*VV] PWd DEP a. œneta * b. œeta *! c. œta *!

2.3.6.3.1 Pronouns

The productivity of liaison is most clear in relation to the pronominal system. The anti-hiatus constraint has a strong effect throughout the paradigm, perhaps unlike other varieties of French, and it is often satisfied by liaison, as for on ‘we’ in Tableau 5 and vous ‘you (pl.)’ in Tableau 6.

Tableau 5. On a on a ‘we have’ Stratum 1 /ѐ#Ϫ/ MAX [*VV] PWd DEP a. ѐnϪ * b. ѐϪ *! c. ѐ *!

Tableau 6. Vous avez vous avez ‘you (pl.) have’ Stratum 1 /vu#ave/ MAX [*VV] PWd DEP a. vuzave * b. vuave *! c. vuve *!

The third pronoun that can be followed by a liaison consonant is ça ‘it’, although unlike with other grammatical persons, liaison is variable. Indeed of 15 cases of subject ça followed by a verb, five have liaison. A single token has hiatus, while in the remaining cases hiatus is resolved through vowel deletion. The reasons for this variability are not entirely clear, but it is important to note that this / l/ epenthesis is quite stigmatized and so its absence in some contexts may be due to the effect of prescriptivism. With this in mind, a constraint * l will preliminarily be placed in equal rank to the anti-hiatus constraint, making use for the time being of crucially unranked constraints (Anttila 1997, indicated through thick broken lines) as a simple way to obtain variation (see Chapter 4 for a much more detailed discussion of variation).

Tableau 7. Ça a ça a ‘it has’ Stratum 1 /sa#Ϫ/ MAX *l [*VV] PWd DEP a. salϪ * * b. saϪ * c. sa *!

With respect to pronouns not accompanied by liaison, Tableau 8 shows what occurs for 1 st p. sg. As I discuss in section 2.2.1, I adopt the view that there is no underlying schwa in items such as je ‘I’, meaning that liaison would not be expected.

Tableau 8. J’ai j’ai ‘I have’ Stratum 1 /Ћ#e/ MAX [*VV] PWd DEP a. Ћe b. Ћœe *! *

In the case of the 2 nd p. sg., liaison might be expected to intervene between tu ‘you’ and a vowel- initial verb. Instead, and unexpectedly given the high ranked MAX constraint, the pronoun’s vowel is absent. But there is reason to believe that the alternation between the short form, in t’as ‘you have’ for instance, and the full form is due to allomorphy. Evidence for this comes from the absence of affrication, unlike in other cases (discussed in section 3.1.2) where a high front vowel has deleted following a / t/ or / d/.

Tableau 9. Tu as tu as ‘you have’ Stratum 1 /{ t, ty}# Ϫ/ MAX [*VV] PWd DEP a. tϪ b. tyϪ *!

The situation for il(s) ‘he/they’ is perhaps more complex than for other pronouns. As discussed in 2.3.2, SF requires liaison after ils , but this never occurs in the data. In all the cases of ils before a vowel-initial verb, the pronoun surfaces as [ j]. The same situation holds for the singular pronoun: the SF [ l] never appears, suggesting strongly that it is absent from the underlying form.

But adopting / i/ as underlying for il(s) raises the question of why a liaison consonant is not free to intervene between the pronoun and verb. But as has been documented, QF high vowels, and in

40 particular high front vowels, are very susceptible to lenition. Section 4.3 contains a proposal for dealing with this, but for now a constraint [*-back, high] (No high front vowels) will be ranked above DEP and a general constraint IDENT (All features in the output must be identical to features in the input), as in Tableau 10 and Tableau 11.

Tableau 10. Il a il a ‘he has’ Stratum 1 /i#Ϫ/ MAX [*VV] PWd [*-back, high] DEP IDENT a. jϪ * b. iϪ *! * c. ilϪ *! *

Tableau 11. Ils ont ils ont ‘they (masc.) have’ Stratum 1 /i#ѐ/ MAX [*VV] PWd [*-back, high] DEP IDENT a. jѐ * b. iѐ *! * c. izѐ *! *

Finally, Tableau 12 gives the situation for elle ‘she’. Even though the pronoun often surfaces without a final consonant, I assume that in this pre-vocalic context it has an underlying final consonant. A more detailed discussion of this is in 4.2.3.

Tableau 12. Elle a elle a ‘she has’ Stratum 1 /al#Ϫ/ MAX [*VV] PWd DEP a. alϪ b. aϪ *! *

This is the entirety of the pronominal paradigm in colloquial QF. Nous ‘we’ is not used as a subject pronoun, and the masculine plural pronoun is used rather than elles ‘they (fem.)’.

2.3.6.4 Constraints for variable epenthetic liaison

With respect to optional liaison, as discussed in 2.3.4 the only pocket of robust variation involves the present tense forms of the verb être ‘to be’. This liaison is on the one hand a much more restricted version of a general pattern in other varieties wherein a large number of verbs are followed by a liaison / t/, and on the other hand represents the generalizing of a pattern in that SF

41 requires / z/ for 1 st and 2 nd p. sg. suis and es . Côté (2005a) suggests that this / t/ has a similar status to the plural / z/ (as in Tableau 2). However, there are two reasons for giving it a different kind of analysis. First, it does not seem to have content in the same way as the plural prefix, which can be thought of as carrying number. Second, its strong variability is an argument for giving it epenthetic status rather than conceiving of it as underlying, since in the latter scenario there is no reason it would not surface. As mentioned, I attribute the possibility of liaison with these verbs to the existence of an anti-hiatus constraint targeting phonological phrases. This constraint will, for the time being once again, be crucially unranked with respect to DEP , as in Tableau 13.

Tableau 13. Est un est un ‘is a’ Stratum 1 /etœ/ MAX [*VV] PWd DEP [*VV] PPh a. eœ * b. etœ *

2.3.6.5 Consonant choice

Up to this point, I have not concerned myself with accounting for the choice of liaison consonant, which is of course the great hurdle that an epenthesis-based account must clear. I have argued that this choice is more predictable than not, but it remains the case that a number of consonant choices do not fit any broader pattern, which is what has lead most researchers to assume that liaison consonants are underlyingly specified. However, there are a few very strong generalizations that come out of the data. Within obligatory liaison, the assumption that nasal vowels are followed by / n/ and that / z/ otherwise appears accounts for 214 of 257 cases. Accepting the proposals for dealing with certain prenominal adjectives (as in Tableau 3) and ils (as in Tableau 11) in conjunction with the assumption that the consonants in peut-être ‘maybe’ and temps en temps ‘sometimes’ are underlying as well as the stipulation that ça ‘it’ is followed by / l/ brings the total up to 251 of 257 cases of consonant choice being predicted. There is good reason to believe that a learner would be sensitive to such a strong pattern and would make use of representations and constraints/rules that most efficiently accounted for the pattern. Here this would mean a process of epenthesis, with / z/ serving as the default consonant and a constraint such as v → n (A nasal vowel is followed by n).

This in no way minimizes the importance of accounting for the cases that are exceptions to this pattern. For obligatory liaison there are either three or four types of exceptions in I’s data, depending on the analysis. There are four tokens with the sequence as in 66, and single tokens of 67 through 69, although 69 represents a whole category of exceptions that we would expect to find with more data.

66. [...] il va en faire des affaires. [vϪna] ‘he’s going to do (some) things’ (I/28/26)

67. Je suis si contente d’avoir de tes nouvelles et encore plus de savoir [...] [enakѐr] ‘I’m so happy to get news from you and even happier to know [...] (I/22/1)

68. Fait vingt ans bientôt, R., j’en reviens pas. cf. vingt [ vϯ] ‘twenty’ [vϯta] ‘It’s twenty years soon, R., I can’t get over it.’ (I/38/1)

69. [...] le petit ange , là, a été choisi [...] cf. petit [ p(œ)tsi] ‘small (masc.)’ [ptsitaЋ] ‘the little (masc.) angel got chosen’ (I/16/58)

66 and 67 taken in conjunction may give the impression that there is a secondary generalization imposing [ n] as liaison consonant preceding a nasal vowel. But it seems that this approach would run into problems. In particular, if [ z] is a default consonant, the wrong prediction would be made about sequences such as in il nous en donne [nuza] ‘he gives us some’. Also, examples such as 70, uttered by C., cast doubt about whether the consonant preceding en should be thought of as epenthetic.

70. [...] elle en vend, elle. [ana] ‘she sells some’ (C/I50)

In that sequence, consonant epenthesis would not have been necessary, as [ ala] could have surfaced and been identical to what was underlying. For these reasons, it seems best to treat en as having two allomorphs / a/ and /na/ (in addition to / za/: see 2.3.6.2). As for 67, it is most likely a production error. 68 is particularly interesting since it is an exception to both the pattern of [ n]

43 following a nasal vowel and to [ z] preceding a plural noun. 69 has a prenominal adjective similarly to previously discussed items, but no denasalization is involved, as is the case for other frequent adjectives such as grand ‘big’. This means that the allomorphy analysis is not necessary, and in fact Côté argues based on acoustic evidence that an epenthesis analysis is preferable.

The variable liaison category (summarized in Table 4) has a far lesser proportion of predictable cases, although the generalization about nasal vowels does come through. For variable liaison, consonant choice is bound up with defining the contexts that can and cannot have liaison, especially because of how lexically determined this seems to be in the data here. It seems that there should be a similar answer for why sont ‘are’ is followed by [ t] and not [ n] as for why était ‘was’ is not followed by a consonant at all. Contrasts such as the auxiliary ont ‘have’ not taking liaison while sont ‘are’ can suggest that no strictly phonological, prosodic or syntactic account will work. Instead, it seems that in QF optional liaison contexts will have to be defined in lexical terms, as indeed seems to be the case for consonant choice.

If these exceptional cases of liaison are analyzed as epenthetic, then lexically-specified constraints will be necessary in order for the correct consonant to be chosen. The alternative is to have any consonant that doesn’t fit the general consonant choice pattern be underlying. The considerations presented so far suggest that the first approach is better, but the evidence is not overwhelming. Moreover, neither solution is particularly appealing since it leads to an analysis that is somewhat fragmentary and stipulative.

But there is no doubt that the adult grammar with respect to liaison is profoundly affected by factors that come into play at later stages of acquisition. These include the influence of prescriptivism, formal education and orthography. These effects may create a grammar that is itself fragmentary and stipulative. It seems possible that learning that dans ‘in’ is followed by a liaison consonant, and that the consonant to choose should be [ z], could lead to representing this information simply in the form of a constraint that captures this single fact. For this reason, in following sections all liaison consonants, other than those in a small group of prenominal adjectives, in the plural marker, and with certain clitics such as / na/ and / za/, will be treated as epenthetic.

2.4 What is needed going forward

The discussion of fixed final consonants and schwa reached similar conclusions. In some ways, the data are fairly straightforward. With fixed final consonants and with the vast majority of schwa insertion, hiatus simply does not occur. In general, fixed final consonants do not delete, unless preceded by another consonant, and schwa appears to break up bad consonant clusters. However, both phenomena gave early indications of a feature of the data that is far less simple: variability. Both the patterning of schwa and the behaviour of avec with fixed final consonants suggest that variation will be an important aspect to the analysis here. In contrast, liaison raises a number of difficulties that must be addressed, and accounts of liaison will be provided in Chapter 5. Certainly variability is also fundamental to the liaison data, as the discussion of optional liaison showed. However, an equally important aspect of liaison is the extent to which it is categorical in some contexts. On this basis, it will be crucial that the proposed analysis not only be able to predict variation in the data but also its absence. This sets the stage for Chapter 4 and Chapter 5: in the first, variability is addressed; in the second, an explanation for categorical behaviour is proposed. But before moving on to these aspects of the analysis, Chapter 3 will deal with the instances where hiatus would be expected.

Chapter 3 Hiatus Expected

In Chapter 2, I discussed cases where no hiatus would have been expected following the patterns of Standard French (SF). In this chapter, I turn to the cases where SF would impose hiatus. As we will see, Québécois French (QF) on the one hand makes use of a number of anti-hiatus repairs in order to avoid these sequences and on the other tolerates hiatus in many instances. The chapter is divided into two parts: in the first (3.1) I discuss regular cases of expected hiatus and in the second (3.2) I discuss a small class of exceptional cases, the so-called h-aspiré items.

3.1 Regular hiatus

In the casual speech that makes up this study, there are a huge number of contexts where hiatus could reasonably be expected following SF patterns.18 For the main speaker, these make up over 20% of tokens of all vowel-initial words (full extraction tokens: see 1.2.2.1) and over 70% of tokens roughly corresponding to underlying VV sequences (partial extraction tokens). 19 These cases range from examples such as 71 and 72, where there is rather a weak syntactic link between the vowel-final and vowel-initial word, to examples such as 73 and 74, where the words have a much tighter grammatical bond.

71. [...] la moindre petite chose que N t’a dit, “on le sait bien c’est toi”, tout de suite, toi t’as réagis [...] [ dziѐ] (=/ di#ѐ/) ‘the tiniest little thing that N told you, “we know perfectly well it’s you”, right away, you reacted’ (I/16/51)

72. [...] qu’il fait frais, on est bien. [fలϯѐ] (=/ fలϯ#ѐ/) ‘that it’s cool, we’re comfortable.’ (I/34/10)

18 The extent to which hiatus avoidance would be considered non-standard varies according to context, in ways that I discuss further on.

19 full extraction: 339 hiatus not expected, 90 hiatus expected; partial: 207 hiatus not expected, 522 hiatus expected.

73. Mais je l’ ai aimé . [eϯme] (=/ e#ϯme/) ‘But I loved him.’ (I/5/47)

74. [...] quand je suis partie en Allemagne. [paలtsia] (=/ paలti#a/) ‘when I left for Germany.’ (I/27/20)

In 71 and 72, the vowel-final words belong to a different clause than the vowel-initial words that follow them, whereas in 73 the hiatus sequence spans an auxiliary and participle and in 74 a participle and complement preposition. Despite these differences in context, in all of these examples formal SF would require that the vowels be pronounced as a hiatus (Walker 2001), and in these instances that is what is in the data.

However, in the same contexts, there are many other possible outcomes, as the various cases of hiatus resolution in 75-80 illustrate. 20

75. [...] c’est toi qui est exclue. [kje] (=/ ki#e/) ‘it’s you who’s excluded’ (I/15/10)

76. J’ai pensé à ce que tu disais hier, p’is [...] [pase⍝a] (=/ pase#a/) ‘I thought about what you were saying yesterday, and [...] (I/4/10)

77. [...] le mardi , on se voit à pei- on se voit à peine [...] [maలdzijѐ] (=/ maలdi#ѐ/) ‘Tuesdays, we hardly see each oth- we hardly see each other’ (I/37/19)

78. [...] tu continues à parler avec ta chum. [kѐtsna] (=/ kѐtiny#a/) ‘you keep talking to your friend’ (I/1/11)

79. Moi je trouve ça tellement beau une femme enceinte. [bon] (=/ bo#yn/) ‘Me, I find that so beautiful, a pregnant woman.’ (I/28/19)

20 See sections 3.1.1 and 3.1.2 for details of how the different kinds of hiatus resolution (as well as hiatus) were defined.

80. Non, mais vous avez une maison à Montréal. [meza] (=/ mezѐ#a/) ‘No, but you have a house in Montreal.’ (I/11/39)

In 75, a high front V1 becomes the equivalent glide, in a way similar to 76 where V1 and V2 combine to form a diphthong. In 77, a high front glide is inserted between V1 and V2. 78 and 79 show examples of vowel deletion, with V1 deleting in the first case and V2 in the second. In 80, V1 and V2 coalesce to produce a vowel that has some features of V1 and some of V2. In all of these examples, V1 and V2 belong to the same clause, in structures that are very similar to the hiatus tokens in 73 and 74. 76 has a participle followed by a preposition just like in 74, while 78 and 80 also have prepositions, following respectively a present tense verb and a noun. The other cases have a relative pronoun followed by an auxiliary (75), a noun followed by a subject pronoun (77), and an adjective followed by a determiner (79).

3.1.1 Telling hiatus, diphthongs and deleted vowels apart

An obvious first question to address has to do with how transcriptions for these sequences were established. In particular, there does not seem to be consensus in the literature about whether there even exists a consistent method for differentiating hiatus from gliding/diphthongization. But at the same time, based on my intutions as a native speaker of Canadian French, there seems to be a clear difference between the form [ jϪdzi] and [ iϪdzi]. In the first case, this would be understood as il a dit ‘he said’, but in the second it seems much more likely to be understood as a partial utterance consisting of lui a dit ‘said to him/her’.

Based on this, listening to the sequences played a very important part in determining whether or not they were a hiatus. The next step involved comparing the acoustics using Praat (Boersma & Weenink 2006). There seemed to be a good correlation between the perception of sounds as hiatus and the presence of two amplitude peaks in the waveform, as in Figure 2.

Figure 2. Example of hiatus in Praat

mu"Ѐ k ѐ m s Ϫ a vϯk m e z a m i Moi, je comme ça, avec mes amis. ‘Me, I’m like that, with my friends.’ (I/4/127)

Sequences that sounded like diphthongs had one amplitude peak in the waveform, while the spectrogram showed formant movement corresponding to a change in vowel quality from the beginning of the sound to the end.

Figure 3. Example of diphthong in Praat

mϯ vu"z o tvu z ϯ t je p Ϫ⍝a b j e œ m Mais vous-autres, vous étiez pas habillés [...] ‘but you guys, you weren’t dressed’ (I/46/7)

The phonetic criteria for categorizing a sequence as having glide formation (vowel-glide or glide-vowel sequences) were identical to those described above for diphthongization. Throughout this thesis, diphthong sequences will be distinguished from ones involving glides, but for reasons of phonology, not phonetics. Unless otherwise specified, I use the term ‘diphthong’ to refer to a situation where two vowels make up the nucleus of a syllable, whereas I assume that in most cases, glide formation will result in one of the vowels occupying the onset or

49 the coda of a syllable. For instance, for the highlighted portion and the syllable that follows in Figure 3 above, the syllable structure would be as in Figure 4 below.

Figure 4. Syllable structure for diphthong and glide

σ σ

O R O R

N N p Ϫ a b j e

In the first syllable, the diphthong is represented as a branching nucleus, and in the second syllable, the glide occupies the second position in a branching onset.

In some exceptional cases, there is good reason to think that a glide can occupy the nucleus of a syllable (as in the first syllable in Figure 4 instead of the second). For instance, this possibility can be used to explain differences in the behaviour of glide-initial words with respect to sandhi phenomena like liaison and schwa (Côté 2005b: 47). Some of them (as in 81) behave essentially like vowel-initial words, by imposing liaison and barring schwa insertion, while others (as in 82) behave essentially like consonant-initial words, by barring liaison and triggering schwa insertion.

81. oiseau un oiseau l’oiseau cf. âne un âne l’âne [wazo] [œnwazo] [lwazo] [Ϫn] [œnϪn] [lϪn] ‘bird’ ‘a bird’ ‘the bird’ ‘donkey’ ‘a donkey’ ‘the donkey’

82. whisky un whisky le whisky cf. verre un verre le verre [wiski] [œwiski] [lœwiski] [vϯల] [œvϯల] [lœvϯల] ‘whisky’ ‘a whisky’ ‘(the) whisky’ ‘glass’ ‘a glass’ ‘the glass’

Claiming that the glide in 81 is in the nucleus but the one in 82 is in the onset allows for an explanation of the differences in behaviour, since the words that behave like vowel-initial words have the same structure as regular vowel-initial words. This analysis also explains some constraints on possible onsets in French: see Côté 2005b. But having glides in the nucleus is very much the exception. It is reasonable to assume that wherever a glide can occupy an onset or a coda, it will do so.

Like cases of diphthong and glide formation, cases that sounded like one of the two vowels had been deleted (or had coalesced into a third vowel) had a single peak in the waveform, but they had formants that remained (more) stable through the sound. Vowel quality was verified with the help of Appendix A, which gives the average F1 and F2 for the speaker and was created to help in this task. In this way, examples of deletion, such as in Figure 5, could be teased apart from cases of coalescence as well as gliding and diphthong formation.

Figure 5. Example of V2 deletion in Praat

me s p Ϫ s e l ѐ (=/ mϯ#s#e#pϪ#ase#lѐ/) Mais, c’est pas assez long. ‘But it’s not long enough.’ (I/36/8)

Finally, some cases that had two amplitude peaks as would be expected for hiatus tokens were instead analyzed as involving glide insertion: here, the formants corresponding to one or both of the underlying vowels were less stable than for hiatus. These glide insertion cases again relied heavily on listening. To return to a previous example, although the phonetic differences may not be clear-cut, the sequence [ ijϪdzi] would most likely be interpreted in a third way which differs from both the hiatus and the glide realization presented above, as il lui a dit ‘he said to him/her’. While these criteria were applied consistently, it is nonetheless important to bear in mind that these distinctions, particularly between hiatus and diphthong, are perhaps not as reliable as we might like.

3.1.2 Vowel deletion really?

,y, ௙, u ,گ ,Many of the examples of vowel deletion involve the loss of one of the high vowels ( i

Ѩ), as in 78 and 79 repeated below as 83 and 84.

83. [...] tu continues à parler avec ta chum. [kѐtsna] (=/ kѐtiny#a/) ‘you keep talking to your friend’ (I/1/11)

84. Moi je trouve ça tellement beau une femme enceinte. [bon] (=/ bo#yn/) ‘Me, I find that so beautiful, a pregnant woman.’ (I/28/19)

High vowel deletion has been reported for QF (Cedergren & Simoneau 1985, among others), although it is described as happening in weak prosodic positions adjacent to at least one voiceless segment, as for the devoiced vowel in 85 but unlike 83 and 84 above.

85. [...] qui sort jamais. [ki" sѐల] (=/ ki#sѐల/) ‘who never goes out’ (I/11/n.t.)

Nonetheless, because devoicing has been used to account for cases of apparent deletion in other languages such as Japanese (Hirayama 2009 and references therein), it seems reasonable to worry that perhaps these vowels that seem to delete are in fact devoicing.

This issue is particularly important because vowel deletion appears to involve opacity in a way similar to that described for liaison in section 2.3.5. In examples such as 86 and 87, the deletion of a high vowel does not entail the absence of assibilation as might be expected.

86. T’aurais-tu aimé mieux du quatre roues, ou? [ts ϯme] (=/ ty#ϯme/) ‘Would you have preferred ATVing, or?’ (I/39/22)

87. Je l’ai dit aux filles [...] [dz o] (=/ di#o/) ‘I told the girls’ (I/52/1)

In the regular case, QF assibilation applies to dental stops before high, front vowels in a categorical way within a word and variably across a word boundary, but never before other vowels. In the examples above though, on the surface it seems that assibilation is taking place before mid vowels. Because this doesn’t happen elsewhere in the data and because we know that there are high vowels in the underlying form, a better explanation is that the assibilation is as usual due to the high vowels, even though they delete. Here vowel deletion appears to counter- bleed assibilation, which would mean that these two processes interact in a way that involves

52 opacity (see the discussion of opacity in Chapter 5, and in particular the treatment of assibilation in 5.2.1).

But if the high vowels were just devoicing, all of the problems opacity causes (see Chapter 5) could be avoided, as Figure 6 illustrates using the form in 86.

Figure 6. Devoicing, deletion and opacity DEVOICING DELETION

/ty#ϯme/ /ty#ϯme/ I. assibilation IIa. devoicing I. assibilation IIb. deletion tsyϯme ty"ϯme tsyϯme tϯme IIa. devoicing I. assibilation IIb. deletion I. assibilation tsy"ϯme tsy"ϯme tsϯme --- [tsy"ϯme] [tsy"ϯme] [tsϯme] *[ tϯme] Same outcome : the order Crucial ordering : assibilation doesn’t matter must precede deletion

If a devoicing analysis were warranted, there would not be crucial ordering of assibilation and devoicing, as shown on the left. Whether assibilation applies first and then devoicing, or vice versa, the desired outcome obtains. Putting aside the derivational terminology, this means that assibilation and devoicing could apply simultaneously, making these facts fit neatly within the assumptions of classical Optimality Theory (OT). On the other hand, if as shown on the right the vowel truly deletes, then we get the correct surface form if assibilation applies before deletion, but not if the order is the other way around. This more complex relationship between the two processes would require modifications to classical OT.

3.1.2.1 Clear acoustic evidence for devoicing?

The strongest piece of evidence that the high vowels in hiatus context were devoiced rather than deleted would be if they exhibited formant structure despite the absence of voicing. None of the relevant vowels were found to do so: there is simply nothing on the spectrogram corresponding to the underlying high vowel. Figure 7 shows an example of this for the utterance in 86 above: the deleted / y/ which would have appeared as the sixth segment, between [ ts] and [ ϯ], is absent.

Figure 7. Spectrogram and waveform showing high vowel deletion

t ѐ ల ϯ ts ϯ m e m j ø d z(y")k a t ల u t w a u

This is different from some high vowels in the context Cedergren and Simoneau describe as triggering devoicing, as in the spectrogram in Figure 8 below which corresponds to 85 above.

Figure 8. Spectrogram and waveform showing high vowel devoicing

k i" s ѐ ల Ћ a m ϯ

In this spectrogram, unlike those for hiatus contexts, there is some evidence of formants corresponding to the high vowel [ i], despite the absence of a voice bar.

There are two other kinds of evidence that would have pointed quite clearly to some of the high vowels in hiatus context being devoiced rather than deleted. In work on European French final vowel devoicing, Smith (2003) finds partial devoicing to be a common intermediate step between a regular vowel and a fully devoiced one. If devoicing in hiatus context were a frequent process in QF, we would most likely expect to also find cases of partial devoicing (on the spectrogram, a voice bar that disappears part way through the vowel). I did not find such cases in

54 the data. Finally, in Japanese (Hirayama 2009 and references therein) one possible cue to devoicing involves the presence of frication following a stop, corresponding to an underlying stop-high vowel sequence. The QF data show no such pattern.

3.1.2.2 Subtle acoustic evidence for devoicing?

There is not strong acoustic evidence pointing to a need to analyze as devoiced the high vowels that seem to delete. Nonetheless, it might be possible to find some less prominent acoustic cues pointing to the fact that the high vowels have not fully deleted. In looking for this, I sought to compare sequences where a high vowel appears to have deleted with sequences that on the surface seem identical but do not involve deletion. If for instance the [ sѐ] corresponding to si on

/si#ѐ/ ‘if we’ were radically different from the [ sѐ] corresponding to son /sѐ/ ‘his’ then perhaps it would be better not to view the / i/ as having fully deleted. To allow for comparison, the high vowel had to be the first of the two underlying vowels, and the resulting sequence had to be relatively frequent in QF words. The example in 88 for instance was not useful given the lack of words with [ kలœ] in them.

88. [...] j’écris un petit mot [...] [Ћekలœ] (=/ Ћ#ekలi#œ/) ‘I write a note’ (I/25/5)

I identified three different sequences that allowed for comparison, [ sѐ], [ pa] and [ na]. In the first two cases, the deleted vowel in the /VV/ sequences is [ i], and in the latter it is [ y]. The first comparison points were formant values (F1 and F2) for the subsequent vowel. Cedergren & Simoneau (1985) report that on spectrograms of QF devoiced vowels only F2 is visible. I hypothesized that if the seemingly deleted vowels were actually devoiced their F2 value might be discernible. F1 was also compared in case some effect could be found there. In all cases, the deleted or devoiced high vowels have on average lower F1 and higher F2 than the [ ѐ] or [ a] that follows, although the difference for F2 is small between [ i] and [ a] and exceedingly small between [ y] and [ a]. Appendix A gives these averages, which are in keeping with previous findings for QF (Martin 2002). Given these values, if the non-audible high vowels have an effect on the neighbouring sound, we would expect lowering of F1, and most importantly raising of F2. Table 5 gives the results for all three sequences.

Table 5. Formant values for /CVV/ and /CV/ sequences [pa] [sѐ] [na] underlying underlying underlying underlying underlying underlying CVV (N=2) CV (N=10) CVV (N=2) CV (N=10) CVV (N=1) CV (N=10) avg. F1 588 722 754 766 684 628 (Hz) avg. F2 1194 1212 1580 1986 1618 1930 (Hz) example si on sont p’is après pattes continues à Nadine [nگsѐ] [sѐ] [papలϯ] [pat] [kѐtsi"na] [nadz] ‘if we’ ‘are (3pp)’ ‘and after’ ‘legs’ ‘continue to’ ‘Nadine’

The results are not at all as would be expected if the vowels were devoiced and had an effect on the formant value. In all sequences, where F2 would be expected to be higher with underlying CVV sequences, we get lower F2 with the inaudible vowels. For F1, where we may or may not expect an effect, the results are more mixed. With [ pa], the situation is as might be expected if a devoiced vowel were having an effect. The same is true of [ sѐ], although the difference in average F1 is very small. Finally, [ na] shows the opposite effect of what might have been expected if the vowel in the underlying CVV sequence were devoiced. Overall, these findings certainly do not support the devoicing view.

In one of the sequences, [ sѐ], the presence of a fricative also allows for comparison. Smith (2003) cites previous work that claims that French devoiced high vowels can manifest as voiceless fricatives. Similarly, Japanese fricatives preceding a devoiced vowel may also have some differences (Hirayama 2009). Based on this, we may expect that if devoicing were taking place, fricatives preceding the relevant high vowel would be longer than, or would differ in some other way from, fricatives not preceding a high vowel. Table 6 gives the duration of the fricative [s] and the following vowel [ ѐ] in sequences with an underlying high vowel and for sequences without the intervening high vowel, and Table 7 compares the centre of gravity (COG) for [ s] preceding inaudible [ i] followed by [ ѐ] with that for [ s] simply followed by [ ѐ] as well as that for

[s] followed by regular [ i].

Table 6. Duration comparison for [ sѐ] underlying CVV underlying CV (N=2) (N=10) word(s) fric. dur. V dur. word(s) fric. dur. V dur. (sec.) (sec.) (sec.) (sec.) si on 0.034 0.042 façon 0.056 0.055 si on 0.064 0.057 son 0.059 0.119 sont 0.058 0.085 façon 0.057 0.044 sont 0.079 0.11 son 0.075 0.139 sont 0.064 0.098 sont 0.1 0.225 son 0.066 0.135 son 0.064 0.127 AVG 0.049 0.05 AVG 0.068 0.114

Table 7. Centre of gravity comparison for [ sѐ] and [ si] average COG (Hz) N underlying CVV [sѐ] /siѐ/ 5756.6 2 underlying CV [sѐ] /sѐ/ 6725.2 10 underlying CV [si] /si/ 7619.7 10

Neither table contains data that would seem to support the devoicing view. In the case of duration (Table 6), the fricatives preceding high vowels underlyingly are not longer than those simply preceding the nasal vowel, in fact they are shorter on average. With respect to centre of gravity (Table 7), while there does appear to be some difference between the sequences with an underlying high vowel and the plain ones, the comparison with fricatives preceding plain high vowels suggests that the difference is not an effect of a devoiced vowel. If the lower COG in sequences with inaudible high vowels followed by nasals was the result of the high vowel, we would expect that sequences with normal high vowels would also have a lower COG, but instead their COG is higher. Therefore neither comparison point for the fricative supports the view that the high vowels are devoiced rather than deleted.

3.1.2.3 Conclusions about devoicing

The data offer no evidence whatsoever that the high vowels that appear to delete are in fact devoicing. In the absence of such evidence it seems preferable to adopt the simpler view that these high vowels are in fact deleting. Additional support for this comes from the distributional

57 facts. Because QF high vowels devoice and non-high vowels don’t, 21 if deletion and devoicing were on a continuum or were one and the same, we should expect fewer high vowels to surface than non-high vowels. Table 8 gives the rates of deletion for high and non-high vowels, for cases where hiatus would be expected but excluding coalescence.

Table 8. Deletion rate of high and non-high vowels High vowels Non-high vowels V1 N Deleted 40 79 N Not deleted 228 326 Deletion rate 17.5% 24.2% V2 N Deleted 19 61 N Not deleted 119 474 Deletion rate 16% 12.9% Overall Deletion rate 17% 17.5%

The rate of apparent deletion for high vowels is actually lower than that for non-high vowels, which is not at all what would be expected if they were actually devoicing. Instead, this suggests that a better analysis is that there is true deletion of these high vowels, which is qualitatively different from the devoicing that occurs in different contexts.

It is worth noting that even in the absence of any evidence for the devoicing analysis, it might still be possible to adopt that view for theoretical convenience (to handle the opacity described above for instance). Many have advocated for approaches in which the output of the phonology is quite abstract (Prince & Smolensky’s (2004) Containment for instance), suggesting there is not an a priori reason to reject an output such as a voiceless vowel ultimately corresponding to the absence of any acoustic signal. This relates to the fundamental issue of where the line between phonetics and phonology ought to be drawn. The approach advocated for in Chapter 5 allows this issue to be skirted in a sense. Due to the involvement of multiple levels, one could choose to consider, for instance, that the first three levels belong to phonology proper but that the rest are phonetics. A different view of the split would not change the model in a fundamental way.

But a question that remains fundamental is what kinds of computation either module can perform. Let us assume a view of phonetics in which voiceless vowels could serve as input, with some of them being eventually deleted altogether and others surfacing as true voiceless vowels

21 Or perhaps do, but to a far, far lesser extent.

58 depending on the context. For such an approach to correctly predict the attested outputs, it would need to be a formal system just like the phonology. Differentiating between the contexts that allow for devoicing and those that would trigger deletion requires access to the wider segmental context as well as prosodic factors, and involves the manipulation of abstract entities, which is at odds with any notion that phonetics would involve a simple one-to-one mapping of an input from the phonology to an output in the real world. So even if at the phonology-phonetics boundary, the eventually deleted vowels were marked as voiceless, a formal account of how the deletion occurs would still be necessary and is therefore part of the analysis presented here. Moreover, unless there is a good reason to consider that there is a part of phonetics that looks just like phonology but isn’t, a simpler explanation is that these processes are indeed phonological.

3.1.2.3.1 The relationship between phonetics and phonology

In discussing the relationship between phonetics and phonology, I am adopting a fairly traditional view of phonetics as physical implementation and phonology as abstract patterns (see Ohala 1990, 2005 for a description of this view, despite his adoption of a different perspective). To put it another way, “ phonology deals in discrete symbolic elements, while phonetics deals in numbers (on continuous dimensions)” (Keating 1996: 263). The distinction is certainly not as sharp as this may seem to imply, as the large literature about the ways in which phonetics and phonology interact attests to (see Kingston 2007 and references therein).

Because of the corpus-based nature of the data here, some might want to view certain processes under study as phonetic rather than phonological, in that they seem “rather ‘low-level’, perhaps gradient or more variable” (Tucker & Warner 2010, 290). Categorizing some aspects of the data (types of coalescence for instance: see 3.1.6.5) as in some sense phonetic is not in and of itself problematic, as discussed above. However the implication that these aspects do not belong in a phonological model, and must be relegated to a more mechanistic phonetic explanation, is a problem. In considering the scope of phonology, it is my view that it is better to take the first part of Keating’s definition as a starting point rather than the second: computations that take place over ‘discrete symbolic elements’ are part of the phonology, while attempting to excise the effect of factors dealing with ‘numbers on continuous dimensions’ is futile.

Thus the example of high vowel deletion in hiatus context from above, despite being variable, does indeed belong to phonology, in that it involves a clear conditioned alternation between high

59 vowels and Ø. Aspects of the data that similarly involve abstract processes and discrete elements are similarly phonological, even if they are variable and subject to factors like rate of speech. Nonetheless, there are aspects of phonetics that are distinct from phonology: the representations provided in this thesis assume a subsequent stage of phonetic implementation (transcriptions do not show timing, gestural overlap, or any such physical detail).

Whether an aspect of the data belongs within the proposed model or within a latter stage of phonetic implementation is often entirely dependent on how it is analyzed. For instance, as described in previous sections, French high vowels undergo a process of devoicing in certain contexts. This devoicing is gradient: some vowels devoice partially, others fully, and their duration also varies. In this case, it is reasonable to analyze the deletion of a portion of vowels within this same context as the result of phonetic implementation: these are the shortest, most devoiced vowels along a spectrum. These deleted high vowels are the same at the surface as the ones in hiatus context, and correspond to the same underlying forms, but the latter should be treated phonologically while the former could be dealt with outside of the phonology. Similarly, a vowel may surface as raised, or laxed, or similarly modified, because of a phonological effect (like the deletion or addition of a feature), or because of a phonetic effect (like producing higher F1 than usual for the target). Again, where in the analysis the effect belongs depends on whether symbolic elements (in this case, features) are involved.

3.1.3 Preliminary statistics

Following the criteria and considerations outlined in 3.1.1 and 3.1.2, the overall distribution of tokens where hiatus would be expected is shown in Figure 9.

Figure 9. Distribution of hiatus expected tokens

V deletion (N=247) Hiatus (N=204) Diphthongization & gliding (N=183) Coalescence (N=13) Glide insertion (N=11) Combination (N=10)

The data represented in Figure 9 all correspond to /V#V/, and any tokens that might be controversial or skew the results have been removed. Cases corresponding to obligatory liaison environments are excluded, even if they do not have liaison (see 2.3.2 for a description of these tokens). The same is true for environments that were truly variable for liaison (see 2.3.4). However, the tokens for environments that are supposedly optional for liaison, but for which this speaker never has liaison, are included here. This includes for instance cases following a verb other than être ‘to be’ in the present tense, as well as cases of r as liaison consonant. The proposed treatment of liaison would rule it out in these contexts, meaning that such tokens ought to be just as likely to have hiatus, or to employ one of the anti-hiatus processes, as any other. Further exclusions from the numbers in Figure 9 are tokens involving the absence of schwa (see 2.2), and so-called h-aspiré items (see 3.2). Also, instances where either word is il , il y or ils , or where the first word is elle , are also not included here (see 4.2.3). Finally, tokens of two identical vowels yielding a single long vowel are excluded given that it might be misleading to label them as coalescence or deletion. This leaves a total of 668 tokens represented in Figure 9.

Figure 9 is striking in a number of respects. The deletion ratio is very large: it represents the greatest portion of the data, although if the tokens of il, il y and ils had been included, the diphthongization and gliding category would have been the biggest. Since vowel deletion is a relatively non-standard method of hiatus avoidance, the fact that so many of these non-liaison vowel-vowel contexts result in deletion may be surprising. Figure 10 gives the breakdown for deletion, according to which vowel deletes.

Figure 10. Distribution of vowel deletion tokens

V1 deletion (N=134) V2 deletion (N=95) V deletion: V1=V2 (N=15) V1 and V2 deletion (N=2)

Clearly either V1 or V2 is a candidate for deletion, with deletion of the first of two vowels more frequent but by no means overwhelmingly so. Returning to Figure 9, despite the high rates of deletion, the proportion of hiatus is also very substantial. This is an important point to bear in mind: any tendency to avoid hiatus in QF is by no means categorical. The third most likely outcome, diphthong or glide formation, also represents a large number of tokens. The three most frequent forms represent the bulk of the data and have quite similar rates. However, coalescence and glide insertion are relatively rare. This unbalanced distribution is another key aspect of the data. The last category, labeled as ‘combination’, covers cases where more than one anti-hiatus process is used (as in 89 below), or where one of the relevant processes applies but hiatus still results.

89. On s’entend que je choisirai pas le gars qui a un kick [...] [kjœ] (=/ ki#Ϫ#œ/) ‘we can agree that I’m not going to pick a guy who has a crush [...]’ (I/8/32)

In 89, both vowel deletion and gliding have applied. These cases represent a small portion of the data, and do not pattern in any particular way, so they can be treated with other instances of the same process in the future.

3.1.4 The beginnings of an analysis

The following sections will serve to lay the groundwork for the discussion in Chapter 4 (Variation) and Chapter 5 (Opacity) and the analysis that stems from this. The fundamental objective involves establishing an explanatory/predictive model to account for the choice between hiatus, diphthong formation, deletion and any other possible outcome of underlying vowel-vowel sequences. Assuming (until Chapter 4) that such a model will involve constraints, the goal of this section is to set up the kinds of constraints that will be necessary. Within the data set where we would expect hiatus following SF, it seems that all of hiatus, diphthongization/gliding, deletion/coalescence and insertion are at least marginally possible in all contexts. This means that regardless of the frequency for any given output, our constraint set must be able to produce a ranking to select it. In section 3.1.5 I discuss the competition between hiatus and diphthongization/gliding as well as glide insertion, while in 3.1.6 I discuss deletion and coalescence.

3.1.5 Hiatus versus diphthong and glide

As discussed in 3.1.1, the tokens that were identified as cases of hiatus differed from those involving gliding or diphthong formation in that the former had two amplitude peaks while the latter had one. In this respect diphthongs and glides behave just like simple vowels in that they have a single sonority peak, or nucleus. The creation of a diphthong (or glide-vowel, or vowel- glide, sequence) from an underlying VV sequence can therefore be considered to violate a general constraint forcing every vowel to make up a nucleus (V=NUCLEUS : see 4.2 for more discussion and exact formulation). While both diphthongization and glide formation violate

V=NUCLEUS , an additional constraint seems warranted given that diphthongs involve two vowels occupying a single nucleus (see 3.1.1). The fact that this kind of structure is dispreferred is clear from the ease with which high vowels transform into their glide equivalent as compared with the relative rarity of diphthong creation. A markedness constraint NODIPHTHONG (again, see 4.2 for more discussion of the constraint and how it is formulated) penalizes syllables with this kind of non-optimal structure.

Additionally, while glide insertion seems to be a close relative of glide formation, it does not violate V=NUCLEUS , since in the process of glide insertion the two underlying vowels remain sonority peaks at the surface. Glide formation does of course violate at least one faithfulness constraint, seeing as the resulting sequence differs from the underlying one. At minimum, a constraint against the insertion of a segment has been violated. This constraint can be referred to as DEP ROOT NODE , and its converse anti-deletion constraint as MAX ROOT NODE . The constraints are simply intended to capture the fact that the insertion and deletion of segments can be independent from the insertion and deletion of features. As we will see for deletion (see 4.3.1 in particular), features are best thought of as independent from segments, but able to associate with them through mechanisms such as spreading. For this reason, some cases of glide insertion could be considered to only involve a violation of DEP ROOT NODE , on the assumption that all of the features of the epenthetic glide are provided to it by the adjacent high vowel. This depends of course on whether the glide derives its non-vowel status simply from position, or if an additional feature is inserted to distinguish glides and vowels. We can remain agnostic on this point for the time being, and consider DEP ROOT NODE to be the anti-glide insertion constraint.

It is worth mentioning that this analysis of glide insertion assumes the simplest explanation from the point of view of the process involved. In the case of, for instance, an underlying sequence / ai/ which surfaces as [ aji], the assumption from the discussion above is that a root node is inserted (to break up hiatus), which then takes on the features of the subsequent high vowel. This can be expressed as / ai/ aCi [aji], although the derivational appearance of the formulation is not truly meant to imply that there are multiple stages involved. An alternate account in which the high vowel first becomes a glide, after which a high vowel is inserted (/ ai/ aj [aji]), is also conceivable. This approach works far less well for multiple reasons, perhaps most strikingly the absence of motivation for the vowel epenthesis, but the possibility of alternate analysis should be noted.

3.1.6 Deletion and Coalescence

The numerous cases of vowel deletion naturally raise the question of how best to represent the deletion of a segment. At a minimum, this ought to involve MAX ROOT NODE , the constraint against deleting a segment from 3.1.5, but also presumably something more that deals with the fact that all that contributes to vowel quality is lost. From the early days of generative phonology, it has been proposed that this vowel quality can be attributed to features (Jakobson, Fant & Halle 1952, Chomsky & Halle 1968). However, many different views have been put forward about how best to model the features in the inventory of a given language (see Hall 2007, and the references therein). Because the decision about which features make up the QF vowel inventory will affect the analysis of vowel deletion in particular, I looked for sources of evidence in other aspects of QF, and in particular coalescence.

The coalescence data can serve as good evidence about which features QF vowels bear. The data contain a small but not insignificant number of cases where an underlying hiatus sequence results in a single surface vowel that is different from both underlying vowels. A logical assumption would be that the resulting vowel owes its features to the underlying vowels. It would however be possible to adopt the less constraining view that the surface vowel need not match V1 or V2, perhaps because of some adjustment (laxing, tensing, nasalization, etc.). But if this were so, the quality of the resulting vowel should in general be unpredictable. This is not the situation for the vast majority of the coalescence data. In most cases where a vowel that is identical to neither underlying hiatus vowels surfaces, it takes some of its characteristics from V1 and some from V2

64 and has no additional ones. For instance, in the example given above in 80 and repeated below as 90, the resulting vowel has the nasality of V1 and the other characteristics of V2.

90. Non, mais vous avez une maison à Montréal. [meza] (=/ mezѐ#a/) ‘No, but you have a house in Montreal.’ (I/11/39)

Below I sketch out how these cases should be treated, but accepting for the moment that the logical analysis of the coalescence cases is that the vowel features are recombining, it makes most sense to view vowel deletion as the deletion of all of the features of a given underlying vowel. This means that it becomes very important to find a principled way of assigning features to the vowel inventory of QF, since the constraints targeting these features are the ones that will be needed for the analyses in Chapter 4 and Chapter 5.

3.1.6.1 Contrastive Hierarchy

Previous work on QF has tended to be rather stipulative in establishing vowel features. In a recent representative example (Poliquin 2006), each vowel is specified + or – for each feature and is uniquely specified, but there isn’t a clear rationale as to how the feature specifications were arrived at. This type of approach leads to some serious problems related to learnability. In particular, how does a learner determine which features to use to specify vowels? Given the dramatic cross-linguistic differences with respect to the size of consonant and vowel inventories (see for instance Crystal 1997:169-70), as well as the fact that phonetically similar sounds can behave quite differently within the phonologies of different languages (see for instance the discussion of German and Czech h in Dresher 2009: 49-50), it does not seem tenable to attribute this to innate knowledge. Rather, the learner must be in a position to extract this information from the speech he/she is exposed to.

Even if the phonetics could predictably point to a correct set of features, this would not be the end of the story, since it is not necessarily the case that each vowel should have a positive or negative value for each feature. Let us take for instance the low vowels of QF, [ a], [ a] and [ Ϫ]. Because only nasality differentiates the first two from one another, it seems safe to presume that a feature targeting that property will be necessary, although this could take the form of [ a] being marked as [nasal], or [ a] being marked as [–nasal] and [ a] being marked as [+nasal]. But since

[Ϫ] doesn’t have a nasal counterpart, how would we know whether a learner would be motivated to mark it as [–nasal]? With respect to other features, if all three vowels were marked as [+low], why would a learner bother specifying them as [–high]?

Dresher’s (2009) approach to contrast deals with these questions in a principled way: the choice of features is a consequence of the specific inventory of a language and is partially determined by phonological activity. Central to this approach is the notion of the contrastive hierarchy, determined through the successive division algorithm (SDA). Features are only assigned if they serve to create contrast, which is the case if they differentiate at least one segment from the rest of the inventory. Features have a hierarchical organization, with certain features having scope over others. Through the SDA, the inventory is split according to the highest-ranking feature, and then the second feature applies so long as it is contrastive within at least one of the two halves of the inventory created by the first split.

Of fundamental importance for my purposes is the hypothesis that only these contrastive features are phonologically active (the Contrastivist Hypothesis: Hall 2007). So for instance, in a three- vowel inventory, we would at most expect to see two features that are active in the phonology, since the SDA would first split off one of the vowels from the other two with a feature A, and then distinguish the other two from each other with a feature B. Of course given the inventory of French many more features are needed to uniquely specify each V. This theory has the advantage of constraining the possible feature values assigned to the vowel inventory of QF, and provides a sounder foundation to address issues of cognitive plausibility and learnability than arbitrarily assigning features.

3.1.6.2 The coalescence data

The assumption that only contrastive features are phonologically active can guide us in establishing feature specifications based on the coalescence data. If V1 and V2 coalesce to V3, the features of V1 and V2 that are needed to create V3 are phonologically active, and therefore contrastive. Table 9 summarizes every case of coalescence in the data.

Table 9. Cases of coalescence in the data V1 V2 resulting V # of cases 1 i a ϯ 3 2 y o u 1 3 y a œ 1 22 4 e a ϯ 1 5 e œ ϯ 1 6 e o u 1 7 a e ϯ 16 8 a o ѐ 1 23 9 a e ϯ 2 10 Ϫ y ø 1 11 Ϫ y œ 1 12 ѐ a a 1

Some of these coalescence cases seem to suggest in a straightforward way that the feature combination approach is correct. In line 2, for instance, phonetically V1 is high, V2 is back, both are tense and round, and we end up with a high, back, tense, round V3. In line 12, as discussed above, V3 is identical to V2 except that it also has the nasality of V1. Even though these cases may intuitively seem simple to model, there are a number of important issues that need to be addressed in order to do so. I now turn to the first of these issues: whether features are binary or privative.

3.1.6.3 Binary vs. privative features

Whether contrastive features are present in the phonology in both negative and positive form (binary: [+F] and [–F]) or in only one of the two (privative: [+F] or [–F]) will have an important effect on the constraint set. The view that features are binary and the view that they are privative are both compatible with the Contrastive hierarchy (Dresher 2009, Hall 2007), but the data here seem to point to privative features as preferable. Before the case for this can be made, a discussion of what will make up the core of the coalescence data is necessary.

22 These are collapsed with the cases in line 7 in further discussion.

23 This number would have been much higher if instances of dans les ‘in the’ had been extracted.

The types of coalescence in lines 1, 7 and 9 not only have multiple tokens but also seem quite natural based on speaker judgments. For these reasons, I will call these cases robust coalescence, as opposed to other coalescence. In settling the issue of whether features are privative or binary, I take the tokens of robust coalescence to be reliable evidence, whereas, as will become clear, the other coalescence data may not be as solid.

The robust case with the strongest quantitative weight involves the tokens where the coalescence of [ a] and [ e] gives [ ϯ] (lines 4 and 7 in Table 9). It is first worth noting that most of these examples correspond to the sequence elle est ‘she is’, which surfaces simply as [ ϯ]. To analyze these cases as coalescence may raise some eyebrows considering that in some varieties of French the underlying vowel in both elle and est is [ ϯ]. But this is not the case in most informal registers of QF, including the one here: the elle clitic always surfaces as either [ a] or [ al]. For est , at least for the speaker here, only [ e] is possible with the other pronouns in the paradigm ( il ‘he’ N=21, on ‘we’ N=26, qui ‘who’ N=12).

So if [ a] and [ e] combine to give [ ϯ] what features are involved? One way to think about this is to look at what differentiates V1 from V3, presuming that this difference comes from V2, and to repeat with what differentiates V2 from V3. Table 10 gives a full set of impressionistic phonetic features for QF vowels to guide the establishment of phonological representations.

Table 10. Phonetic features for the QF vowel inventory i y e ø ϯ ϯ œ œ a a Ϫ ѐ ѐ o u [ATR] + + + + – – – – – – – – – + + [back] – – – – – – – – – – + + + + + [high] + + – – – – – – – – – – – – + [low] – – – – – – – – + + + – – – – [nasal] – – – – – + – + – + – – + – – [round] – + – + – – + + – – – + + + +

These are as given in Poliquin (2006: 4-5), 24 with two exceptions: the lax high vowels are not included for the time being because they are not present underlyingly, and length is ignored. Returning to [ a] and [ ϯ], they are very similar, with the one difference being that [ a] is lower than

24 And very similar to those provided in Côté (2005b: 29).

[ϯ], which we can attribute to a feature [low]. [ e] and [ ϯ] are also very similar, differing only in that the former is tense and the latter lax. 25 Following recent practice (Poliquin 2006 among others), I will attribute this difference to the feature [ATR].

The fundamental problem with using binary features for this coalescence is that the [ ϯ] that results from coalescence has to be both [–low] and [–ATR] to keep it distinct from V1 and V2, but it can’t get these values following the assumptions I have presented so far. It has to have both those features, because if it is not [–low], it will not be distinct from [ a], and if it is not [–ATR], it will not be distinct from [ e]. This means, following the model of coalescence set out, that it has to get the feature [–low] from [ e] and the feature [–ATR] from [ a], meaning that those vowels have to be contrastively assigned those features. But there is no proper contrastive hierarchy in which [ a] bears the feature [–ATR] and [ e] bears the feature [–low], as Figure 11 and Figure 12 suggest.

25 This contrast has been amply described in the literature (see Côté 2005 and references therein).

Figure 11. binary [low] > [ATR] low ATR a +

e – + [+low] [–low] – – a a Ϫ ϯ

[+ATR] [–ATR]

i y e ø o u ϯ ϯ œ œ ѐ ѐ

Figure 12. binary [ATR] > [low] ATR low a – +

e + [+ATR] [–ATR] – – i y e ø o u ϯ

[+low] [–low]

a a Ϫ ϯ ϯ œ œ ѐ ѐ

Figure 11 and Figure 12 show the two possibilities for the relationship between [ATR] and [low] in a contrastive hierarchy. In the first tree, vowels are split according to [low], followed by [ATR], if the resulting subgroups can be split according to this feature. In the second tree, the order of feature assignment is reversed, and vowels are only split according to [low] if the feature can serve to separate segments. If [low] is ranked above [ATR] as in Figure 11, [ a] does not get a value for [ATR] since this feature cannot serve to create a contrast amongst low vowels. If [low] is ranked below [ATR] as in Figure 12, [ e] does not get a value for [low] because [low] is not contrastive for the tense vowels. But in the two situations [ ϯ] is specified for both features. Figure 11 and Figure 12 give the situation as though [ATR] and [low] had to be the two highest ranking features, which is not the case, but the same situation obtains no matter how other features are ranked. 26 As Figure 13 and Figure 14 show, this means that there can be no account of coalescence with binary features that is compatible with the contrastive hierarchy and the assumption that the features of the resulting segments come from the underlying segments.

26 This was verified using CoalMiner (Scott & St-Amand 2009).

Figure 13. Coalescence with binary [low] > [ATR] a + e = ϯ +low –low –low +ATR –ATR

Figure 14. Coalescence with binary [ATR] > [low] a + e = ϯ –ATR +ATR –ATR +low –low

This coalescence is sufficient to show the problems with binary features, but a second robust coalescence in line 9 is identical except that the resulting vowel is nasal, like V1 but unlike V2. This additional fact can be dealt with simply, meaning that this second robust coalescence is support for the existence of the first.

The situation for privative features is much better than for binary features. There are many sets of feature specifications that can work for this coalescence, especially if we include the possibility of having negative privative features (i.e. [–F] without a [+F] equivalent). For instance, the hierarchy (split into two parts following the first division) and values in Figure 15 produce a situation where the vowel that results from coalescence gets all of its features from V1 and V2.

Figure 15. Hypothetical privative feature hierarchy

feature hierarchy: [high] > [ATR] > [low] > [back] > [round] > [nasal] active values: [–ATR], [+back], [+high], [+low], [+nasal], [–round]

[+high]

[+round] (-round) u

[+back] (–back) i y

(– high)

[–ATR] (+ATR)

[+low] (–low)

[+back] (–back) [+back] (–back) [+back] (–back) Ϫ o

[–round] (+round) [–round] (+round) e ø

[+nas] (–nas) [+nas] (–nas) [+nas] (–nas) [+nas] (–nas) a a ѐ ѐ ϯ ϯ œ œ

The tree can be read from the bottom up, by finding the terminal node with the vowel of interest, and following it to the top, making note of the privative features it receives. The opposite values of the privative features are shown in italics for reference purposes: these are not active features on segments. For the three vowels in the coalescence, all of which are in the second part of the hierarchy given at the bottom, we get [–ATR, +low] for [ a], [–round] for [ e], and [–ATR,

–round] for [ ϯ]. In this case the resulting vowel only has two features, the first of which it gets from V1 and the second of which it gets from V2, as in Figure 16.

Figure 16. Coalescence with hypothetical privative feature hierarchy a + e = ϯ –ATR –round –ATR +low –round

This is much better than the situation with binary values, since no new features have to be introduced (I discuss the leftover [+low] in 3.1.6.4 below). For the coalescence data, the use of privative rather than binary features allows for a plausible model of the process: the resulting vowel is composed of features in the same way as underlying vowels, and these features do not come from thin air but directly from the input to coalescence.

3.1.6.4 Further issues with privative features

One important question that arises from the approach discussed above is, if [ a] ([–ATR, +low]) combines with [ e] ([–round]) to produce [ ϯ] ([–ATR, –round]), what happens to the [+low] feature that [ a] has but [ ϯ] doesn’t? Although it is conceivable that a process of feature deletion could come into play, it seems preferable to posit a system without stipulations about which features could delete and which couldn’t. This is one of many serious restrictions on this model of how coalescence occurs: for a coalescence to be considered successfully accounted for, there cannot be an additional feature left over.

There are a few situations where it seems clear that the deletion of features would be well motivated and predictable. These involve the combination of features that are inherently incompatible. Given the size of the vowel inventory of QF, many combinations of features that aren’t possible in other languages are present. For instance, the back ~ front contrast is more independent of the rounded ~ unrounded contrast than in languages with smaller inventories. But this is not the case for the features relating to height: [high] and [low], as well as [ATR] which essentially creates a third height category given that it serves to contrast the mid-high vowels from the mid-low vowels (see section 7.3.3 of Dresher (2009) and Calabrese (2005) for discussion of similar inventories). While [+high] and [+ATR] can coexist (with high vowels possibly being specified for both depending on the feature hierarchy), no [+low] vowel could ever also bear the feature [+high] or [+ATR] in QF. Given this, it is a simple addition to the system that if V1 is [+high] or [+ATR] (or both) and V2 is [+low], or vice versa, these features cancel each other out (see Calabrese 2005). For an account of a coalescence to be considered

73 successful, both V1 and V2 have to contribute at least one feature to the resulting vowel. This means that, for instance, in the system given above in Figure 15, [ ø] could never be V1 or V2 because it does not have any contrastive features, and thus nothing to contribute. In cases where there is a canceling of height features, this was considered a contribution to the coalescence.

3.1.6.5 Choosing the best set of privative features

Given the restrictions on coalescence described above, as well as the feature cancellation process, there are still a large number of possible feature rankings and privative feature value sets that produce the three robust coalescences, [ a] + [ e] = [ ϯ], [ a] + [ e] = [ ϯ] and [ i] + [ a] = [ ϯ]. The question becomes which of the possibilities goes the furthest in accounting for the other coalescences as well as the rest of what we know about the phonology of QF. Using a computer program written for this purpose (CoalMiner: Scott & St-Amand 2009), it was possible to sort through all of the possible sets of privative values and hierarchies to determine which was best. This was done with the assumption that if a vowel were to be specified for a given feature it would be as in Table 10, repeated below as Table 11.27

27 It was pointed out to me (Brousseau, p.c.) that the pronunciation of the nasal low vowel is variable with respect to backness: some speakers may have a more central or back version, while for others the backness varies. The average F2 value for the main speaker’s [ a] does indeed suggest that the vowel is not as front as other front vowels (see Appendix A) or that its backness varies. The F2 values do not however warrant considering it [+back] (i.e. [ Ϫ]). In any case, in the successful feature hierarchy (in 3.1.6.6), it is not specified for [back]. This is the desirable outcome, given the variation in pronunciation along this dimension.

Table 11. Non-contrastive specifications used for CoalMiner i y e ø ϯ ϯ œ œ a a Ϫ ѐ ѐ o u [ATR] + + + + – – – – – – – – – + + [back] – – – – – – – – – – + + + + + [high] + + – – – – – – – – – – – – + [low] – – – – – – – – + + + – – – – [nasal] – – – – – + – + – + – – + – – [round] – + – + – – + + – – – + + + +

Two fundamental facts involving QF vowels should also be compatible with the specifications established on the basis of the coalescence data: high vowel laxing and assibilation. The complication is that all three high vowels lax, whereas only the two front high vowels trigger assibilation. This means that there should be one (or more) feature that differentiates all of the high vowels from all of the other vowels, and one (or more) feature that distinguishes the front vowels from the back ones. This may not seem such a lofty requirement, but the use of privative features means that in half the possible situations the active feature is [–high], which would fail to capture high vowel laxing, since the high vowels would not have a feature in common. The same is true in the case of assibilation, where the contrastive hierarchy approach also means that a large number of rankings and values fail. For instance, if [–back] is chosen as the active feature and [back] is ranked below [round] we could end up with only [ y] being marked as [–back]. This would fail to capture assibilation, since there would not be a feature that [ y] and [ i] shared but [ u] did not.

Strikingly, both the best ranking and values for the coalescence data as determined by CoalMiner, as well as the ranking and values that are second best for the coalescence data but superior in some crucial respects, have the natural classes necessary for laxing and assibilation. Given the enormous number of possible results CoalMiner could have arrived at, this offers further support for the approach.

The most successful ranking and values for the coalescence data account for the three robust cases and are able to handle four of the other coalescences, with the addition of a minor caveat discussed below. However, a ranking and set of values that do only slightly worse with the coalescence data turn out to be far preferable with respect to other phenomena, most notably vowel deletion (see Chapter 4). Because the trade-off seems warranted, I adopt the hierarchy that fares slightly worse with the coalescence data, and describe it in 3.1.6.6 below. A description of

75 the hierarchy that performs best when only the coalescence data are taken into account is included in Appendix B. Appendix C and Appendix D provide further discussion of issues related to the hierarchy that is optimal based solely on the coalescence data: the first explores the possibility of using one bivalent feature, while the second discusses the benefits of assigning features hierarchically, looking only at coalescence. Before describing the ranking and values that best fit the data, an additional remark is warranted.

The robust coalescence cases clearly belong in phonology proper. Speakers of QF (or some of them, at least) would recognize, for instance, the transformation of / da#le#mϯ/ dans les mains ‘in

(my/your/etc.) hands’ to [ dϯmϯ] as belonging to QF. But many of the other coalescence cases seem quite different. In particular, it does not seem that native speakers would consider the resulting vowel predictable. Given also the rapid rate of speech that undoubtedly contributes to the existence of coalescence, at least some of these other cases seem closer to phonetics than the robust cases. It is an additional restriction on this model that the two kinds of coalescence involve identical mechanisms. This does not preclude us from distinguishing them: for instance, we could posit two kinds of coalescences, one that takes place ‘earlier’ (corresponding to the robust cases) and another that takes place ‘later’ (the other cases). Following the discussion in 3.1.2.3.1 about the distinction between phonetics and phonology, the fact of the non-robust cases having coalescence could be treated within the model (as it involves deletion of an entity), but their vowel quality could result from phonetic implementation. In this case, it might be possible to account for every coalescence token. For the time being, I do not pursue this approach, but tentatively adopt the hierarchy that best accounts for the data. Even if the other coalescence cases require a different explanation, their inclusion here does not affect the outcome, since more weight was given to the robust cases and the other processes of QF phonology.

3.1.6.6 Successful feature hierarchy

The best account for the coalescence data using simple privative values succeeds in handling seven of eleven cases, with the help of a caveat allowing a nasal vowel to bear the feature [+ATR], as detailed in Appendix B. A quite substantial addition to the system that allows some features to have different privative values in different parts of the inventory takes that number up to nine, as described in Appendix C. But as we will see in 4.2 and 4.3, these hierarchies are less successful in dealing with the facts of variable vowel deletion than one that successfully accounts

76 for all of the robust cases of coalescence plus an additional three. This is the hierarchy I will refer to as the successful feature hierarchy.

In this globally superior set all the privative features take on positive values, in order to fit with the vowel deletion data in addition to the coalescence data. The two features that have negative values in the sets that best handle coalescence, [back] and [round], are two of only three features that the best constraint set for vowel deletion requires (see Table 19), but critically the vowel deletion account requires positive values. Since no constraint set with [–back] and/or [–round] did as well with vowel deletion, and since the coalescence cases that this feature set fails to account for constitute weaker evidence (i.e. they are the non-robust cases), it seems far better to adopt this compromise set of contrastive specifications and hierarchy. Also, even though it suffers somewhat with respect to coalescence, the fact that it only uses positive values for privative features is another possible reason to prefer it on the grounds of simplicity and symmetry. Since the adopted feature set contains only privative features with positive values, features will simply be written as [F] rather than [+F].

The set of feature specifications that does best with the most data is in Table 12. A contrastive feature tree for one of a number of possible feature hierarchies that result in these feature specifications is in Figure 17 (the list of these is in Appendix E). The feature hierarchy is in two parts, separated following the first split according to [ATR].

Table 12. Best contrastive specifications using privative features i y e ø ϯ ϯ œ œ a a Ϫ ѐ ѐ o u [ATR] [back] [high] [low] [nasal] [round] Figure 17. Contrastive hierarchy for best constraint set

feature hierarchy: [ATR] > [high] > [low] > [round] > [back] > [nasal]

[ATR]

[high] (–high)

[round] (-round) [round] (-round) i e

[back] (–back) [back] (–back) u y o ø

(–ATR)

[low] (–low)

[round] (-round)

[back] (–back) [back] (–back) Ϫ

[nas] (–nas) [nas] (–nas) [nas] (–nas) [nas] (–nas) a a ѐ ѐ œ œ ϯ ϯ

As discussed for Figure 15, the features for a segment can be read off the tree from the bottom to the top, by making note of the bracketed features it receives. The negative values are shown for reference purposes only: they do not constitute active features.

The details of the coalescences according to this feature hierarchy are in Figure 18. The robust coalescences are mostly handled through the cancelling of height features (see 3.1.6.4), shown with strikethrough. For cases where the feature set does not seem adequate, the problematic features are circled.

Figure 18. Coalescences with best set of feature specifications

Robust coalescences: all successfully accounted for 1. a. a + e = ϯ b. a + e = ϯ low ATR low ATR nasal nasal c. i + a = ϯ ATR low high

Other coalescences: successfully accounted for 2. a. y + a = œ b. y + o = u ATR low round ATR ATR ATR high high back back round round round high round c. a + o = ѐ low ATR back back round round

Other coalescences: not successfully accounted for 3. a. ѐ + a = a b. Ϫ + y = œ back low low back ATR round nasal nasal low high round round

c. e + œ = ϯ d. e + o = u ATR nasal nasal ATR ATR ATR round back back round high round e. Ϫ + y = ø back ATR ATR low high round round

This hierarchy works for six cases of coalescence, including all three of the robust cases. The cases in 3 cannot be accounted for with the system here, although as discussed in footnote 27 (in 3.1.6.5), the phonetic realization of / a/ varies greatly, in ways that might impact the treatment of 3a. Although a success rate of slightly more than half could appear weak, it is the result of the constraints imposed on how coalescence is treated. In particular, the requirement that no features be introduced or deleted is clearly too strict, given that vowel adjustment (raising, lowering, rounding) is possible. But the objective here is to use the analysis of coalescence as a jumping off point for establishing a feature hierarchy rather than as an end point. In this respect, the results here are very good, in that the proposed hierarchy handles all the cases that seem to clearly belong to phonology proper (the robust cases) in a way that is compatible with many other facets of QF.

This lays the groundwork for the analysis of coalescence and of the full range of anti-hiatus processes that continues in Chapter 4 and Chapter 5. Although the coalescence cases are small in number, they contribute a lot to framing the analysis that follows. In particular, they offer support for a feature-based analysis, and in conjunction with the vowel deletion facts in Chapter

4 they offer support for a constraint set that includes MAX [ATR], MAX [back], MAX [high], MAX 28 [low], MAX [nasal], MAX [round] . Before moving on to this analysis however, a discussion of the small group of h-aspiré words that is exceptionally linked to hiatus is warranted.

3.2 H-aspiré

A small class of phonetically vowel initial words has long been known to behave anomalously with respect to hiatus phenomena in Standard French as well as in QF. These items, referred to as

28 The analysis adopted here assumes that assigning contrastive features to the inventory (through the contrastive hierarchy and the SDA) takes place in a stage that comes before the first constraint level (Level 1 in Chapter 5). Dresher (2009: 6.4) provides a procedure for converting a feature hierarchy into a set of OT constraints that interleves faithfulness and markedness constraints. This view of the contrastive hierarchy as a constraint ranking fits well with the approach here and is in keeping with Richness of the Base. The resulting constraint level would feed Level 1 in the model proposed in Chapter 5.

80 h-aspiré because most of them begin with an h orthographically, behave like consonant-initial words in a number of respects, as Table 13 summarizes.

Table 13. Comparison of h-aspiré, V-initial, and C-initial forms h-aspiré V-initial C-initial Example in [azar ] hasard [ѐm] homme [ցarsѐ(] garçon isolation form ‘coincidence’ ‘man’ ‘boy’ Definite no yes no determiner [lœazar ] le hasard [lѐm] l’homme [lœցarsѐ(] le garçon ‘the coincidence’ ‘the man’ ‘the boy’ Enchaînement no yes no [kϯl . azar ] quel hasard [kϯ . lѐm] quel homme [kϯl . ցarsѐ(] quel garçon ‘which coincidence’ ‘which man’ ‘which boy’ Liaison no yes no [leazar ] les hasards [lezѐm] les hommes [leցarsѐ(] les garcons ‘the coincidences’ ‘the men’ ‘the boys’ Suppletive no yes no forms [sœazar ] ce hasard ‘this [sϯtѐm] cet homme [sœցarsѐ(] ce garçon coincidence’ ‘this man’ ‘this boy’ [vjøazar ] vieux hasard [vjϯjѐm] vieil homme [vjøցarsѐ(] vieux garçon ‘old coincidence’ ‘old man’ ‘old boy’ (=‘bachelor’) (modified from St-Amand 2006: 4)

Any h-aspiré item appearing in the data would be expected to have hiatus, since it ought to block liaison and resist deletion, as the behaviour of the definite determiner from Table 13 suggests. QF has important differences from SF with respect to which lexical items are treated as h-aspiré. For instance, the verb haïr ‘to hate’ is h-aspiré in SF but not in QF, with the first person singular present tense being pronounced with a schwa in SF ([ Ћђϯ]) but not in QF ([ Ћa(j)i], where the two vowels and the optional glide belong to the verb not the subject clitic). However, the data contain no instances of words that are h-aspiré in SF but never are in QF. Speakers of QF are also known to vary more than those of SF with respect to whether they treat specific items as h-aspiré (Walker 1984, Côté 2005b), which may or may not be a factor here, as we will see below.

3.2.1 H-aspiré data

The data for the main speaker contain very few h-aspiré items. While this means that there may be inadequate evidence for addressing some of the issues related to h-aspiré, some clear conclusions can still be drawn. Table 14 contains the details for all of the h-aspiré items in the data.

Table 14. Data for h-aspiré h-aspiré word Total N Outcome N Example hamac ‘hammock’ 1 No schwa 1 l’hamac [ lamak] ‘the hammock’ (I/20/5) hasard ‘coincidence’ 1 Glide insertion 1 au hasard [ owazaల] ‘at random’ (I/15/13) hâte ‘haste’ 13 Coalescence 1 on a hâte [ ѐnϪ⍧t] ‘we look forward’ (I/15/19) Diphthong 5 j’ai hâte [ Ћe⍝Ϫt] ‘I look forward’ (I/6/32) Glide formation 1 j’ai hâte [ ЋjϪt] ‘I look forward’ (I/37/41) Hiatus 3 j’ai hâte [ ЋeϪt] ‘I look forward’ (I/24/23) V1 deletion 3 j’ai hâte [ ЋϪt] ‘I look forward’ (I/28/12) haut ‘top’ 1 Hiatus 1 en haut [ ao] ‘upstairs’ (I/24/9) homard ‘lobster’ 4 Hiatus 2 le homard [ lѐomaల]29 ‘the lobster’ (I/42/24) V1 deletion 1 du homard [ dzomaల] ‘(some) lobster’ (I/42/16) V2 deletion 1 un homard [ œmaల] ‘a lobster’ (I/42/21) (ks] ‘my (symbol) X’ (I/31/5گx ‘(symbol) X’ 1 Hiatus 1 mon x [ mѐ

At first glance, it seems that many of the tokens do not fit with what would be expected for h- aspiré items. A traditional view of h-aspiré would lead to hiatus being predicted for every token, but only a third of the data actually surface with hiatus. However, given the results in section 3.1, it may not be that surprising that where hiatus would be assumed to occur, a number of anti- hiatus processes appear to apply.

Although the data seem to go against the expected patterns for h-aspiré, they actually differ more fundamentally from the patterns for regular vowel-initial words. Indeed, the three last lexical items all have hiatus in ways that are completely unique for phonetically vowel-initial words. If the h-aspiré phenomenon had not yet been identified, haut , homard , and x would have stood out as exceptions, given that they are respectively the only case of en not triggering liaison (see 2.3.4), the only case of hiatus with the definite determiner (see 2.3.3), and the only case of mon not triggering liaison (see 2.3.2). For this reason, and despite the small number of relevant tokens, it is necessary to include an account of h-aspiré in this analysis. Section 3.2.2 deals with this account.

29 The quality of V1, which would be expected to be [œ], is most likely due to backness assimilation to V2.

3.2.2 Analyzing h-aspiré

There have been many proposals for dealing with h-aspiré throughout the history of French phonology (see St-Amand 2006 for a more in-depth discussion of these). These can be roughly split into two camps: phonological approaches in which h-aspiré words are distinguished by a shared phonological characteristic and lexical or morphological approaches in which h-aspiré words are distinguished by a shared non-phonological characteristic. The small number of tokens here makes an argument in favour of either approach tenuous at best, but nonetheless there does seem to be some support for a phonological treatment.

At the basis of the morphological and lexical approaches, from earlier rule-based treatments (Cornulier 1981, Tranel 1981) to more recent ones inspired by them (Tranel & del Gobbo 2002), is the idea that h-aspiré words either impose hiatus or else must be aligned with a syllable boundary. But the data for homard ‘lobster’ show that neither claim fits with the surface forms. The cases of hiatus with homard are the ones that need explaining in the broader context, but in both of the other types of forms, vowel deletion causes the word homard to neither coincide with a syllable boundary nor impose hiatus. While it might be possible to reconcile an approach based on hiatus imposition or syllable boundaries with the data through the use of multi-level and/or stochastic evaluation, it does not seem like the correct insight would be captured. H-aspiré words are different from other vowel-initial words, but it does not seem like this difference is that they impose hiatus or must be aligned with the beginning of a syllable. Phonological approaches offer a way to capture this difference: h-aspiré words are different from other phonetically vowel- initial words because they do not begin with a vowel.

Many approaches in this direction have been proposed. H-aspiré words have been argued to begin with a consonant that never surfaces (from the earlier approaches of Dell (1973), Schane (1968), Selkirk & Vergnaud (1973) to the more recent Gabriel & Meisenburg (2005) and Boersma (2007): usually / h/ (Schane) or / Б/ (Dell, Gabriel & Meisenburg, Boersma)) or else a unique structure in autosegmental approaches, such as an empty skeletal slot. Once again, there is too little data to favour one approach over another. However, there is a compelling reason for adopting the view that h-aspiré words are best represented as containing a segment / h/ which never surfaces, and this has to do with borrowings. Speakers of QF tend to adapt English words beginning with / h/ as though they were h-aspiré items. Firstly, the consonant itself is never

83 pronounced no matter whether the borrowing is brand new or well established (Paradis & LaCharité 2001). Secondly, h-initial English words tend to block liaison and appear with full definite determiners in just the same way as h-aspiré words do. A potential explanation for these facts could be that QF speakers already have a phoneme / h/, with no phonetic realization, and that they map the English / h/ to it (see Paradis & LaCharité 2001 for an opposing viewpoint).

The robustness of the borrowing facts favours the /h/ analysis, so in the absence of conclusive evidence from the data, I will adopt it here.

3.2.3 The details of the analysis

The proposal that h-aspiré words begin with an / h/ that then deletes is more complex than it may seem. The / h/ must be present long enough to block liaison and impose full definite determiners. However, given that there are a number of cases of vowel deletion in the data, it seems preferable to have / h/ delete at a point in the analysis that would still allow for this deletion to be attributable to hiatus. Although vowels do delete when they are adjacent to consonants only, the rate of deletion is much lower than for hiatus contexts (see 2.1). Table 14 suggests that if there were sufficient data, the numbers for deletion would be more in keeping with hiatus contexts. With the caveat that this situation has been described in a derivational way, it does seem to lend itself to a derivational analysis. Approaches, such as traditional OT, that rely on a single evaluation level would either be incapable of predicting the attested forms, or else would have to rely on ad hoc mechanisms that would mask the generalizations related to all of the relevant processes.

As suggested in section 3.1.2 and explored in detail in Chapter 5, much of the hiatus and hiatus resolution data involve opacity. For this reason, I argue in favour of a multi-level approach. This seems to be precisely what is required for the h-aspiré facts. Given how few tokens there are, this group of words would not be sufficient to motivate the adoption of a more complex model, but other processes warrant it. Given this, the fact that the underlying / h/ seems to be present at some points in the evaluation and absent at others is simply due to its being present for some processes but absent for others.

For the data here, the main processes that require the presence of / h/ are liaison and the choice of definite determiners (see St-Amand 2006 for a similar and more complete treatment). With h- 30 aspiré items, liaison is blocked, and the definite determiner surfaces as [ lœ]. I analyze both the presence of a liaison consonant and of schwa in the definite determiner as the result of epenthesis, following Côté (2008). This means that some aspect of h-aspiré must block the epenthesis of a liaison consonant, but force schwa epenthesis. In the approach to liaison discussed in Chapter 2, the mere presence of a consonant is sufficient to block liaison, since it is driven by hiatus avoidance. Nonetheless, I propose that the main feature of the abstract segment /h/ is that it cannot be adjacent to a consonant, as instantiated through the constraint *h↔C (The 31 32 segment / h/ must not be adjacent to a consonant) which dominates DEP (No insertion). This certainly ensures that liaison consonant epenthesis would never be warranted for an h-aspiré word, but more importantly motivates the insertion of schwa with the definite determiner / l/. So that deletion does not serve to satisfy *h↔C, MAX is undominated on this early level. Once liaison is no longer an option and schwa has been inserted, / h/ deletes since it has no phonetic realization in QF. This is simply attributed to the constraint *h (The segment / h/ must not 33 appear), which must dominate MAX (No deletion).

3.2.3.1 Example tableaux

Tableau 14 through Tableau 16 illustrate the functioning of these constraints and the multi-level approach in a schematic way. Again, Chapter 5 discusses many relevant issues more amply but

30 This is the typical and expected result even though it is not seen here. The divergences in the data are explained elsewhere, and having [ lœ] as intermediate form still makes the most sense.

31 This constraint can be seen as a member of the C ↔V (A consonant must be adjacent to a vowel.) family of constraints (Steriade 1999b, Côté 2000).

32 Informally: see Chapter 5.

33 Informally: see Chapter 5.

85 for the time being, the output of what will be called ‘early level’ here (to avoid confusion with the actual proposed model) becomes the input of ‘late level’. The results for the homard ‘lobster’ forms will be illustrated here, since they involve the four fundamental aspects required: h-aspiré status, the definite determiner, liaison, and vowel deletion. The variable nature of the latter process will temporarily be attributed to crucially unranked constraints as was done for liaison

(see section 2.3.6.4). The anti-hiatus constraint *VV will be tied with MAX , resulting in vowel deletion half the time. The constraints determining which of the two vowels will delete are not included (see Chapter 4): in Tableau 14, V1 deletion is shown as a possibility, but V2 deletion could be too. For the second and third tableaux, the attested form is shown (with V2 deletion in Tableau 15 and V1 deletion in Tableau 16), but deletion of the other vowel would be possible. For the time being the adjustments to V1 in le homard ‘the lobster’ [lѐomaల] and the assibilation in du homard ‘(some) lobster’ [ dzomaల] will be abstracted away from. For the tableaux for the early level the optimal intermediate form is shown with , and for the late levels the attested form (taking into account the abstractions mentioned above) is shown with and the other possible form with .

Tableau 14. Multi-level approach to h-aspiré item: le homard le homard ‘the lobster’ Early level le homard ‘the lobster’ Late level

/l#homaల/ MAX *h↔C DEP lœhomaల *h MAX *VV a. lœhomaల * a. lœomaల * * b. lhomaల *! b. lœhomaల *! c. lomaల *! c. lomaల **

Tableau 15. Multi-level approach to h-aspiré item: un homard un homard ‘a lobster’ Early level un homard ‘a lobster’ Late level

/œ#homaల/ MAX *h↔C DEP œhomaల *h MAX *VV a. œhomaల a. œmaల ** b. œnhomaల *! * b. œhomaల *! c. œomaల * *

Tableau 16. Multi-level approach to h-aspiré item: du homard du homard ‘(some) lobster’ Early level du homard ‘(some) lobster’ Late level

/dy#homaల/ MAX *h↔C DEP dyhomaల *h MAX *VV a. dyhomaల a. domaల ** b. dhomaల *! * b. dyhomaల *! c. dyomaల * *

In general, this set of constraint rankings accounts for schwa epenthesis with clitics and for the absence of liaison consonant epenthesis. For this speaker, it accounts for the behaviour of the words haut , homard , and x. They are all h-aspiré words, begin with / h/, and for the cases when they do not have hiatus, subsequent vowel deletion can explain this.

3.2.3.2 Remaining words not h-aspiré?

The situation is less clear for the rest of the words in Table 14, hamac , hasard , and hâte . None of them has a clear exception to the pattern for regular vowel-initial words. Since the list of lexical items that is h-aspiré varies between speakers, it is possible that for this speaker, the three words are simply not h-aspiré. In this case, their underlying forms begin with vowels and they simply pattern with the rest of the normal vowel-initial words. However, it is also possible that these words are indeed h-aspiré. In this case, they would have / h/ in the early level, and it would subsequently delete. This would be straightforward for the tokens of hasard and hâte , where /h/ would simply intervene between the two underlying vowels, before its deletion triggered the potential application of an anti-hiatus process. But the case of hamac is somewhat complicated by the fact that, if it is h-aspiré, explaining its surface form would require claiming that schwa is inserted and then deleted. This is in fact fine, and such derivations are a prediction of the model, as discussed in 5.4.2.2. Schwa epenthesis would take place in an early level to ensure that the clitic consonant / l/ was not adjacent to /h/, and then schwa deletion would occur in a late level as part of regular vowel deletion. In any case, there is simply too little data to reach a conclusion about whether or not the three words should be considered h-aspiré, but either possibility fits with the analysis.

3.3 What is needed going forward

This chapter’s data, where hiatus would be expected following the rules of SF, reinforce some of the requirements for a successful analysis that were arrived at at the end of the previous chapter. The chapter was split into two major parts: the first dealt with regular contexts that would normally have hiatus and the second the exceptional h-aspiré lexical items. In the first part, we saw that vowel deletion and assibilation interact, in that deletion serves to mask the conditioning for assibilation. How this should be analyzed will be discussed in Chapter 5, but this chapter

87 provided evidence that this process interaction must be explained. In particular, we saw that vowels truly are deleting, and not simply devoicing, and that this deletion occurs frequently.

Another possible outcome of vowel sequences, coalescence, was used to explore how best to assign features to the QF vowel inventory. By imposing very strict criteria for treating coalescence, I found sets of feature specifications that did reasonably well with the coalescence data and were compatible with other aspects of QF phonology. The best set had privative features that were assigned in a way that is compatible with the view that features are organized in a contrastive hierarchy and that only contrastive features are active in the phonology (Dresher

2009, Dresher & Rice 2002). On this basis, constraints of the form MAX [F] will be used in subsequent chapters. We also saw that these regular cases where hiatus would be expected showed robust variability, which Chapter 4 presents an account for.

The second part of the chapter dealt with the h-aspiré data, which are also variable. I argued that h-aspiré items should be analyzed as beginning with an abstract segment (Schane 1968 among many others). I also argued that the best way to model the behaviour of these items was through a multi-level approach (Kiparsky 2000, 2010; Rubach 2000; Bermúdez-Otero, in preparation) such as will be discussed in Chapter 5. In general then, this chapter reinforced the need for a solution that is feature-based, multi-stratal, and able to account for variation.

Chapter 4 Variation

The data in Chapter 2 and Chapter 3 exhibit a wide range of variation. For instance, section 2.1.2.1 contains examples showing the myriad possible surface forms of the lexical item avec ‘with’, repeated here as 91-97 (previously 11-17).

91. [...] p’is elle est en amour avec G. [amuల avϯk] (=/ amuల#avϯk/) ‘and she’s in love with G’ (I/21/10)

92. Je suis pas comme ça avec eux [...] [sϪ vϯk] (=/ sϪ#avϯk/) ‘I’m not like that with them’ (I/4/123)

93. [...] il y a rien avec elle [...] [లjϯ Єϯk] (=/ లjϯ#avϯk/) ‘nothing’s going on with her’ (I/33/17)

94. [...] tu détournes le regard avec un petit sourire [...] [లœցϪల a⍝ϯk] (=/ లœցϪల#avϯk/) ‘you give a sideways glance with a little smile’ (I/1/9)

95. [...] aux femmes enceintes, avec les bébés [...] [asϯt ϯk] (=/ asϯt#avϯk/) ‘to pregnant women, with babies’ (I/28/36)

96. Je parlais de ça avec F tantôt [...] [sϪ ϯjk] (=/ sϪ#avϯk/) ‘I was talking about that with F before’ (I/33/13)

97. Je vas y aller avec toi. [ale k] (=/ ale#avϯk/) ‘I’m going with you.’ (I/17/15)

Based on these data alone, it seems that certain factors likely influence how avec surfaces. For instance, even in the absence of any previous discussion of this fact, it seems that the first vowel of avec is more likely to delete after a vowel (as in 92, 93, 96 and 97) than after a consonant (as in 91, 94 and 95). However, the fact of fundamental importance that the data illustrate is that there isn’t a single factor or series of factors that will successfully predict the exact form that

89 avec will take. 34 Individual factors, such as preceding sound, may appear to have an effect on the probability of a certain outcome, but they do not determine it entirely.

This situation persists, with added complications, in cases where either the first or the second vowel can delete. For instance, there are many possible outcomes when a word ending in / i/ is followed by a word beginning in / Ϫ/. In 98, / i/ deletes, while in 99, a very similar sequence shows no deletion. It is also possible for / Ϫ/ to delete rather than / i/, as in 100. Finally, 101 and 102 show two different outcomes where no deletion takes place, respectively hiatus and glide formation.

98. P’is j’ai dit, “ok” . [dzϪke] (=/ di#Ϫke/) ‘And I said, “ok”.’ (I/2/38)

99. Là les gars ils ont dit, “ok , on va venir dans le spa avec vous-autres”. (/Ϫke] (=/ di#Ϫkeگdz] ‘Then the guys said, “ok, we’ll come into the jacuzzi with you”. (I/27/68)

100. [...] qui a un coup de coeur sur une fille [...] [kjœ] (=/ ki#Ϫ#œ/) ‘who has a crush on a girl’ (I/8/38)

34 Of course, if these seven tokens constituted all of the data, it might be possible to come up with an analysis involving factors such as prosody and neighbouring lexical items that would entirely predict the correct output, but no such analysis would work with the complete data set. Both prosody and lexical factors undoubtedly have an effect on hiatus and hiatus resolution, but not in ways that would explain away the variation in the data. The role of prosody is central in the analysis presented in Chapter 5, but coding each token for its position within prosodic constituents proved too complex for the purposes of this thesis. Because the rate of speech is so rapid, there were very few instances where prosodic boundaries clearly corresponded to a pause. Coding for prosodic constistuency would require calculating the relative duration of segments within the full discourse context, a task that went beyond the scope of this research. In any case, the vast majority of tokens do not occur adjacent to any sort of a pause, meaning that the variability in hiatus cannot be attributed to such a factor.

101. Ils m’ont dit, “ah, ça a de l’air t’as été déçue [...]” [dziϪ] (=/ di#Ϫ/) ‘They told me, “oh, apparently you were disappointed’ (I/23/66)

102. [...] un gars qui dit, “ah, M. je va’s aller t’aider [...]” [dzjϪ] (=/ di#Ϫ/) ‘a guy who says, “oh, M. I’ll go help you’ (I/27/47)

There are again undoubtedly factors we can isolate that contribute to the likelihood of a vowel deleting. However, these data show even more starkly how unlikely it is that a hard and fast rule could be uncovered to predict correct surface forms accurately. This is further complicated by considering forms where V1 and V2 are reversed, corresponding to the underlying form / Ϫi/. In this case, the data contain no deletion at all, but instead mostly glide formation as in 103, with some examples of hiatus as in 104.

103. P’is là, il dit, “bien viens je vas te la présenter [...]” [lϪj] (=/ lϪ#i/) ‘So then, he says, “well, come, I’ll introduce you to her”’ (I/2/19)

104. Ma chum, elle a Illico . [Ϫiliko] (=/ Ϫ#iliko/) ‘My friend, she has Illico [television service].’ (I/11/16)

There is clearly no straightforward answer about why deletion would readily occur with / iϪ/ but not with / Ϫi/.

All of these examples point to the fact that hiatus resolution in Québécois French (QF) involves substantial variation. This chapter focuses in particular on the patterns of variable vowel deletion, because their complexity makes necessary an elaborate model of variation. In Chapter 5, this component of the grammar is integrated into a larger model, which also captures the optionality present in other aspects of the data. Although the extent of the variation may seem to be an oddity, it is to be expected given on the one hand what sociolinguists have described as the inherent variability of language (Labov 1969) and on the fact that many research projects have found robust variability in the phonology, morphology and syntax of QF, in particular those based on the Montreal corpus (Sankoff & Cedergren 1972) and the corpora housed at the University of Ottawa (Poplack 1989, Poplack & St-Amand 2007). This means that a successful analysis of hiatus and hiatus resolution in QF will involve at least one component of the grammar

91 able to model variation. Over the years, many proposals have been put forward for how best to deal with variation, both before the advent of Optimality Theory (section 4.1.1 below) and after it (section 4.1.2 below). We now turn to a discussion of these proposals.

4.1 Models for Accounting for Variation

4.1.1 Before Optimality Theory

The dominant voice in early treatments of phonological variation came from sociolinguistics rather than phonological theory proper. The work of Labov (1966, 1972) shaped the bulk of quantitative research in phonology, as well as in the other linguistic disciplines. Key to this research is the fact that variability is pervasive in all components of language and that this variability is structured. Social factors such as gender, class, race and so forth may have an impact on the frequency with which a speaker uses a given form, but factors internal to language also determine the likelihood that a form gets selected in a given context. The identification of this type of linguistic factor, such as for instance following segment in a phonological study or clause polarity in syntactic research, is what took the study of variation from a static state wherein a speaker’s social characteristics globally shaped their speech patterns to a dynamic situation where every different utterance may have a different outcome.

The grammatical model that was developed to deal with variation was the variable rule (Labov 1969). Variable rules operated similarly to traditional phonological rules but allowed for a probabilistic component to account for the contribution of various factors. For instance, Labov outlines the schwa deletion rule in 105 below (87 in the original), where β and α serve to quantify the contribution of the preceding pronoun and the following verb.

1 105. ђ (Ø) / [ βpro] ## C ## [ αVb] 0 +T (Labov 1969: 739)

Modern sociolinguistic research does not typically present this type of formula in its results, but the variable rule is still centrally involved because of variable rule analysis, which is at the heart of the findings of much contemporary sociolinguistic work. First developed in the work of Cedergren & Sankoff (1974), variable rule analysis remains a leading analytical tool in variationist sociolinguistics, in part due to the wide availability of software to implement it (Goldvarb: Rand & Sankoff 1990; Robinson, Lawrence & Tagliamonte 2001). Variable rule

92 analysis involves multivariate regression and allows researchers to determine which factors affect the choice of a linguistic form and to what extent. Results are typically presented as tables containing factor weights representing the contribution of a factor to the probability that a form will surface. The findings of variationist sociolinguistics are frequently limited to a discussion of the effects of various factors, without a discussion of the linguistic mechanism through which the effects are instantiated. In the absence of such discussion, the implied grammatical model could be taken to be the variable rule, given that it underpins variable rule analysis. However, in the cases where theoretical assumptions are not made clear, it is probably not the case that researchers would truly privilege this kind of modified rule-based analysis over more contemporary models like constraint- or exemplar-based approaches.

Despite this fact it is worth noting that the variable rule approach suffers from some major limitations, making it less than ideal for dealing with the variable hiatus and hiatus resolution data here. Variable rules have all of the same drawbacks and issues as have been described for traditional rule-based approaches. These have been extensively discussed elsewhere (see Kager 1999: 2.1 and references therein) and need not be reviewed as a whole here. The one issue that stands out strongly has to do with conspiracies. In constraint-based models the tendency to avoid vowel-vowel sequences can be stated directly, while a rule-based analysis would require separate rules for each possible way of repairing hiatus. So while *VV as a constraint seems a succinct explanation for the possible surface forms, a rule-based treatment would require numerous rules (V1 deletion, V2 deletion, gliding, consonant/glide insertion and so on) which would appear to have no connection to each other whatsoever. This masks the motivation for these rules, making the approach less explanatory. More importantly, it also severs the link between the various anti- hiatus processes. But as we have seen, many underlying sequences have more than one possible outcome: to write a rule for V1 deletion that is completely independent from the rule for glide formation, for instance, misses the point that both rules are similar options for avoiding an undesirable vowel-vowel sequence. While variable rules would allow for a probabilistic component to be introduced into both rules, there is a probabilistic relationship between the two rules that could not be captured.

Finally, variable rules come with additional problems not shared by other rule-based systems. The fact that a rule such as that given by Labov (see 105 above) might need to be expanded to include many more factors could produce some unwieldy results. It is not clear how such a

93 system could be limited in order to avoid producing far too many possible rules (rules with 100 factors, rules with factors that cancel each other out, etc.). If individuals can have limitless numbers of rules of limitless different forms, any notion of shared dialect, or in fact of language acquisition, becomes very difficult. Because some of the probabilities within each rule have to do with social factors, it seems likely that the default would be for speakers of the same linguistic community to have different rules, and so different grammars. But how does a speaker learn a whole new grammar that resembles but also differs in significant ways from everyone around them, if both the form and the content of each rule are essentially arbitrary? Indeed, with every probability able to take any value (between 0 and 1) and every rule able to refer to any number of factors, the task of settling on a grammar appears to become unmanageable.

4.1.2 After Optimality Theory

The advent of Optimality Theory (OT) brought about some new proposals for dealing with variation. In the best of these, variability stems naturally from the architecture of OT. Unlike the variable rule approach, this means that the ability to handle variation does not seem to be a tacked-on property of the system but is at its very core.

The first influential approach of this kind was that of Anttila (1997). He proposed that variation could be derived by having some constraints be crucially unranked with respect to one another (Kiparsky 1993 and Reynolds 1994 had previously studied crucial unranking). Whereas traditional OT has strict domination of constraints, where every constraint either dominates or is dominated by the other constraints in the constraint set, in Anttila’s model two or more constraints can essentially be tied. In the case of two crucially unranked constraints, both are ranked equally within a single hierarchy level, and at each evaluation their relative ordering is set at random. This leads to a situation where both possible orderings of the two constraints occur in half of all evaluations. For the data here, if hiatus could be attributed to a single constraint A and hiatus avoidance to a single constraint B, a grammar where constraints A and B are crucially unranked would produce hiatus half the time. Other ratios can be obtained by having more than two constraints participate: three or more constraints can be crucially unranked, or else there can be crucially unranked constraints at more than one point in the hierarchy. This approach has many strong points. It accounts for variation without much having to be added to traditional OT. In fact, if anything, it suggests that variation ought to be pervasive throughout the grammar.

While Anttila’s (1997) approach to variation is appealing, it has one major drawback that keeps it from being a good fit for the data here. As also pointed out in Coetzee & Pater (2008), it is ill equipped for dealing with rare variants. Indeed, although it has the ability to get quite uneven ratios between forms (it is no problem for the grammar to output a given form say one sixth of the time), this does not seem sufficient here. While it is not clear that there needs to be a strict matchup between the frequency of a form in the data and the frequency with which a grammatical model outputs it (see section 4.2.5 below), it seems fairly uncontroversial that if a form occurs for instance less than 1% of the time, it is undesirable to predict that it should be occurring more than 10% of the time. But in Anttila’s model, the only way to get frequency predictions in the range of less than 1% would be to use an enormous constraint set, which would take away much of the explanatory appeal of the approach. Because there are many rare variants here (certain pronunciations of avec ‘with’, glide insertion as hiatus repair, etc.), a suitable model must be able to predict that some forms are possible but exceedingly unlikely, which Anttila’s proposal cannot.

A solution to the problem of modeling rare variants came through a spate of probabilistic models, both within OT and Harmonic Grammar, most notably Stochastic OT (for a discussion of the Harmonic Grammar approaches, see Coetzee & Pater 2008). These models incorporated statistical noise to yield results where in a small proportion of cases the usual ranking between two constraints could be reversed (see 4.1.3.1 for a more detailed description), leading to very rare outputs. In fact, these approaches are able to predict any relative ratio for a form, while keeping many of the benefits of Anttila’s early proposal. The ability to handle variation is again not separate from the basic functioning of the model: no additional mechanism is required to account for forms that occur half of the time as opposed to those that occur all the time. As discussed below, for Stochastic OT, variable forms involve constraints that have close ranking values whereas categorical ones are due to constraints that are far apart. Moreover, because there have been probabilistic models proposed for many aspects of human cognition (see for instance Chater, Tenenbaum & Yuille 2006), such models are appealing in that they seem to have independent support from other branches of the cognitive sciences.

4.1.3 Stochastic OT and the Gradual Learning Algorithm

Because the data here have extensive and complex variation, Stochastic OT implemented through the Gradual Learning Algorithm (GLA: Boersma & Hayes 2001) is a good fit. This is

95 particularly the case given the existence of the software package OTSoft (Hayes, Tesar & Zuraw 2003) that allows its users to submit data and constraint sets, and determines an optimal constraint ranking using the GLA. 35 Section 4.2 below presents the results obtained with OTSoft, but a brief discussion of how Stochastic OT, the GLA and OTSoft work precedes it.

4.1.3.1 Stochastic OT, GLA and OTSoft: the basics

The GLA is an appropriate model for variable data because of two fundamental characteristics: it has stochastic evaluation (as discussed in 4.1.2 above), and a continuous ranking scale. Rather than returning a constraint hierarchy where higher ranked constraints strictly dominate lower ranked ones, GLA results involve a numerical value (henceforth ranking value) being assigned to each constraint in the set. The ranking value for a constraint is the mean of a normal distribution from which a constraint’s actual value for a given evaluation is selected. Every time the constraint set is evaluated, a selection point is chosen randomly from within this normal distribution. Let us consider the following hypothetical situation, which greatly simplifies the situation for the relevant data. Given a single constraint *VV that militates against hiatus, and a single constraint MAX V that militates in favour of hiatus (assuming that vowel deletion is the only available way to avoid hiatus), and if *VV has a ranking value of one higher (see below for a discussion of what the ranking values mean and how to interpret the numbers) than MAX V, then for the majority of evaluations, *VV would dominate MAX V. This would lead to vowel deletion rather than hiatus, as a typical case sketched out in Figure 19 illustrates.

Figure 19. Hypothetical relationship between *VV and MAX V leading to deletion

35 OTSoft can also be used with other ranking algorithms, for non-variable data.

However, there would still be a number of evaluations in which MAX V dominates *VV, leading to hiatus, as Figure 20 illustrates.

Figure 20. Hypothetical relationship between *VV and MAX V leading to hiatus

This ranking would model a data set in which deletion is the outcome most of the time, but where hiatus occurs in a not unsubstantial number of cases.

In assessing the results of the GLA, an important factor is interpreting the ranking values that OTSoft returns. The numerical values within the GLA are arbitrary: there is no objective sense to be made of a constraint having the value 80, but rather a constraint’s ranking value should be interpreted in relation to the other constraints in the set. It is an assumption of the GLA that all constraints have the same standard deviation: the shapes of *VV and MAX V in Figure 19 and Figure 20 above are by stipulation identical. This standard deviation is set at 2, which is the key figure in interpreting constraint-ranking numbers. Boersma & Hayes (2001) offer some reminders about properties of the normal distribution to help interpret the numbers: about 68% of the values are within one standard deviation (i.e. 2) of the mean; if a constraint A has a ranking of five standard deviations (i.e. 10) higher than a constraint B, the odds that a higher selection point will be selected for B than A is 1 in 5,000; finally, if A is nine standard deviations (i.e. 18) higher than B, the odds of B having a higher selection point than A is 1 in 10 billion.

4.1.3.2 How the GLA settles on an optimal model

Section 4.1.3.1 above is sufficient for basic interpretation of the results of the GLA returned by OTSoft; however an understanding of some of the intricacies of the functioning of the GLA may also help for the discussion of the results here. For a more detailed description than what follows, see Boersma & Hayes (2001), Hayes (2004), Hayes, Tesar & Zuraw (2003).

A user of OTSoft must provide the program with a candidate set for every underlying form, the constraint set, and every violation mark incurred by a given candidate for a given constraint. Based on these data, the algorithm proceeds to the ranking of constraints. All constraints start off with a value of 100. The first step involves presenting the algorithm with learning data, 36 which is chosen from amongst the correct forms in the candidate set, i.e. candidates with a frequency greater than 0. The GLA operates according to the idealization that the algorithm has direct access to the underlying form for these learning data, following Tesar & Smolensky (1998). The algorithm then uses this underlying form to generate the form that the current constraint ranking would produce. It does this by determining a selection point for each constraint, by adding a component of random noise to the constraint’s ranking value. This step can be thought of as a point being chosen from the normal distribution centred on the ranking value. The constraints are then sorted in order of selection point, and evaluation takes place as in traditional OT. This generated form is then compared to the learning datum. If the two forms are different, the algorithm compares their constraint violations, and decreases the value of the constraints violated by the learning datum but not the generated form, and increases the value of the constraints violated by the generated form but not the learning datum. The size of these increases and decreases is referred to as plasticity. 37 The algorithm repeats these steps a given number of times 38 and settles on a final constraint ranking. Because the ranking values are arbitrary, there is a wide range of potential results. Typically values cluster around 100, but it is quite possible to have a successful run in which one or more constraint has a much higher, or much lower, value.

36 The number of forms considered is one of the parameters the user can set. This is reported as cycles .

37 Plasticity is also a parameter that can be set by the user. All these runs used the default settings, which involve four stages, starting at 2 and decreasing to 0.001.

38 This final parameter is reported as learning trials .

4.2 Preliminary Run

To begin analyzing the data, a preliminary set of constraints described in 4.2.1 and 4.2.2 was submitted to OTSoft. Every OTSoft ranking based on the constraints, candidates and violation marks provided to it will be referred to as a ‘run’. As we will see, small differences with respect to candidates or constraints can lead to major differences in how successful a run is.

4.2.1 Max [V]

To get a preliminary sense of the patterning for the stratum where vowel deletion takes place, a set of constraints that simply penalize the deletion of specific vowels was submitted to OTSoft.

The constraint set includes MAX constraints for each vowel that is present in the underlying VV sequence (henceforth MAX [V] constraints). A candidate that does not have [V], but corresponds to an input with [V], incurs a violation of MAX [V]. This is meant to give a pre-theoretical sketch of the variation. It is not intended as a claim that a constraint such as MAX [i] is operational in the grammar (although this possibility is of course not ruled out a priori ). Instead, the ranking of the various MAX [V] constraints will help determine what kinds of vowels delete more or less easily. Moreover, this run will serve as a baseline with which to compare other runs. Because it involves very little theoretical abstraction, it ought to be able to fit the data relatively successfully. The vowels are not categorized in any way ahead of time, and so OTSoft is free to rank the constraints that target vowels in whatever way required. This ought to come quite close to accounting for the data, given that the ranking and constraint set is doing little more than describing the deletion. 39

39 This run does abstract away the position of the vowels (V1 or V2) from their quality, which does constitute very preliminary analysis. It is conceivable that a given vowel could show a tendency to delete when in V1 position, but not when in V2 position. However, a run with constraints meant to test this (of the type MAX [V] V1/2 (e.g. MAX [u] V1, MAX [e] V2, etc.) does considerably less well than the MAX [V] run described below (with an average error per candidate of 18.098 %). The fact that it does worse is likely due to the large size of the constraint set, but also suggests that this small analytical step is not a problem.

4.2.2 Core constraints

The set also includes the constraints *VV, DEP , V=NUCLEUS , NODIPHTHONG , MAX WORD INITIAL and MAX V1. Candidates with hiatus incur violations of *VV, while those with epenthetic segments, as through the insertion of a glide to break up a VV sequence, violate DEP .

V=N UCLEUS is violated by any output where two input vowels are combined into a single syllable, either through diphthong formation, or through one of the vowels becoming its glide equivalent, while NODIPHTHONG is violated when two input vowels are combined into a single syllable, but neither is able to become a glide. Because the inventory of QF has underlying high glides but not underlying diphthongs except in a very few exceptional cases (see 3.1.1), it seems reasonable that the surfacing of the latter would be a more serious violation of well-formedness. In any case, there is no doubt that it is “easier” for high vowels to become glides then it is for two non-high vowels to combine into a diphthong. Amongst the cases where hiatus is expected (see 3.1.3), and even when the il tokens are excluded (see 4.2.3), tokens where at least one of V1 or V2 is a high vowel are likelier to result in a glide or diphthong than where this isn’t the case, as Table 15 shows.

Table 15. Rates of glide/diphthong formation according to presence of high vowel V1 and/or V2 is high Neither V is high N Glides/Dipthongs 86 104 Total N 227 458 Gliding/Diphthongization rate 37.9% 22.8%

nd MAX WORD INITIAL penalizes the deletion of the 2 vowel in a VV sequence. This is based on the work of Casali (1997, 1998), who finds a cross-linguistic tendency to preserve V2 in hiatus context (see section 4.3.3 below for a discussion of the other factors Casali discusses). He attributes this to importance of the first sound in lexical access, with the presumption that speakers would ease the processing burden to their listener by avoiding the deletion of word- initial phonemes. While this tendency does seem to exist in the data (as the results below suggest), the opposite tendency whereby V1 rather than V2 is preserved also seems to be at play, though perhaps in smaller pockets of the data. For instance the comparison of / Ϫa/ and / aϪ/ sequences in Table 16 suggests that the tendency to preserve V1 in / Ϫa/ is not due to the characteristics of V1 but perhaps simply its position.

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Table 16. Comparison of /Ϫa/ and /aϪ/ /Ϫa/ /aϪ/ N % N % V1 deletion 4 8 0 0 V2 deletion 21 42 7 87.5 Hiatus 8 16 1 12.5 Diphthong 17 34 0 0 Glide insertion 0 0 0 0

For this reason, the constraint MAX V1 was introduced to penalize the deletion of a vowel in this position.

4.2.3 Exclusions

Here, as in all subsequent runs, tokens were combined according to underlying VV sequence in order for all of the data to be handled at once. This means that all of the cases where the main speaker says something corresponding to, for instance, / Ϫa/ are grouped together, regardless of what lexical items they correspond to or of any other difference. Also, for all runs, singleton tokens were excluded because they added unduly to the size of the token files, given their limited usefulness in establishing an overall pattern. Also excluded were the relatively few cases of coalescence, because there would have been a huge number of possible outputs and it would have been difficult to automate the coding of violation marks.

Finally, pre-vocalic tokens of il ‘he’, ils ‘they’ and the existential il y ‘there’ 40 were excluded from this and subsequent runs because they categorically surface as [ j]. As mentioned, high vowels very frequently surface as glides, so this may simply be a reflection of that fact. However, the behaviour of elle ‘she’, the feminine counterpart to il , may best be analyzed as the result of allomorphy. It always surfaces as [ al] pre-vocalically and [ a] pre-consonantally, but neither a categorical rule inserting [ l] between [ a] and a vowel nor a categorical rule deleting [ l] before a consonant seems justified. In the first case, this is because similar contexts, in particular following ça ‘it’ (see 2.3.6.3.1), do not consistently trigger [ l] insertion, and in the second

40 Below, I refer to these as il tokens, since this lexical item makes up the majority of the tokens, and there is no reason to think these words would have different underlying representations.

101 because sequences with [ l] followed by a consonant are very well tolerated in QF. Given these facts, it seems best to assume that elle has two forms: / a/ and / al/. Because il patterns in a similar way, it is possible that the [ j] in the output is underlying, unlike the other VV sequences under study. For this reason, their inclusion had the potential to skew the data, and so they were set aside, though early comparisons between runs suggested that excluding them did not have any major effect on the constraint rankings.

4.2.4 Results of the preliminary run

Table 17 gives the results of the run with MAX [V] constraints, while Figure 21 below it summarizes the formulations of the selected constraints.

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Table 17. Results of preliminary OTSoft run Constraint Ranking Value MAX [u] 148.000 HIGHEST MAX [œ] 142.977 RANKING DEP 140.771

MAX [ѐ] 140.442 MAX [œ] 140.118 MAX [a] 139.866

MAX [ ] 139.131 ϯ MAX [ø] 139.045 MAX [ѐ] 138.477

MAX [o] 138.465

NODIPHTHONG 137.389

MAX WORD INITIAL 137.318

*VV 137.305

MAX [Ϫ] 137.275 MAX V1 136.998 V=N UCLEUS 136.657 MAX [e] 136.121

MAX [i] 135.996

135.890 MAX [ϯ] MAX [a] 135.348 LOWEST MAX [y] 135.074 RANKING MAX [௙] -596.958 average error per candidate: 15.018 cycles: 100 000 learning trials: 1 000 000

Figure 21. Constraint formulations for preliminary OTSoft run

MAX [V]: Every unique vowel in the input must correspond to a unique vowel in the output. DEP : Every segment in the output must correspond to a segment in the input. NODIPHTHONG : A nucleus must not be made up of two vowels. MAX WORD INITIAL : Every word-initial vowel in the input must correspond to a vowel in the output. *VV: Two vowels must not appear in a row. MAX V1: The first of two vowels in a row in the input must correspond to a vowel in the output. V=N UCLEUS : Every segment corresponding to a vowel in the input must make up a unique nucleus.

Two main observations are warranted. The first has to do with the error rate and is discussed in 4.2.5, and the second has to do with the structure of the variation and is discussed in 4.2.6.

4.2.5 Interpreting Error Rates

The average error per candidate (roughly, how well the frequencies in the data are matched) in this preliminary run is much, much higher than any of the error rates reported in Boersma & Hayes (2001). The best results for the three different data sets they use have error rates of 0.11%,

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2.53% and 0.17%, although they do cite higher numbers than this when runs are based on fewer tokens (between 5 and 10 percent).

However, this doesn’t serve to invalidate the results here. These data are very heterogeneous: they represent nearly 100 underlying sequences and include many possible outcomes. Also, despite being based on substantial numbers of tokens, many pockets of the data are sparse, with for instance only two outputs corresponding to a given underlying sequence. The data from Boersma and Hayes are very different: they have vast amounts of data (thousands, if not hundreds of thousands, of forms for each data set) corresponding to only a handful of underlying forms. Having large amounts of homogeneous data drives down error rates considerably, but it is not realistic to expect studies based on natural spoken data to involve the kinds of numbers Boersma and Hayes use.

There is not a vast literature of quantitative research that uses the GLA with which to compare these results. It is worth noting that Clark (2004: Chapter 3) refers to error rates as high as 4.6% as representing satisfactory frequency matching, albeit for very different (syntactic) data. These are the kinds of rates that could have obtained here if different underlying forms had been separated into different runs. It is crucial for the overall purpose of this work that this not be so, given that we are after a single constraint set that best handles all of the data, but this means heterogeneous data, and therefore high error rates. Figure 22 illustrates the effect of both data quantity and heterogeneity by comparing the average error per candidate for a run that only includes the single most frequent underlying forms to that for a run that includes the two most frequent underlying forms, and so on up to the fifteen most frequent (see Appendix F for the complete results of these runs).

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Figure 22. Average error per candidate according to # of most frequent underlying forms

12 10 8 6 4 2 0

Average ErrorAverage per Candidate (%) Top Top Top Top Top Top Top Top Top Top Top Top Top Top Top 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Most Frequent Underlying Forms

Both the run with only the two most frequent underlying forms and the run with only the three most frequent underlying forms have an average error per candidate of less than 5%. The chart also makes clear that the effect of data quantity and heterogeneity compete when adding an additional underlying form. The addition of the form can result in a lower error rate, as is the case for the second underlying form for instance. This is due to the positive effect of increasing the amount of data under consideration. But adding a form can also increase an error rate, as the fourth form exemplifies. This is due to differences between underlying forms and makes the error rate worse. As the overall trend of the chart demonstrates, this effect is stronger than the positive effect of adding data, which is why error rates of less than 5% are lost when all of the data are analyzed at once.

Finally, a comparison of the main speaker with the additional speakers suggests that attempting to match output rates exactly for individual underlying forms may not be that useful. Although the three speakers have similar hiatus and hiatus resolution patterns, their outputs for the main speaker’s most frequent underlying forms differ considerably from the results for the main speaker. Despite having similar rates for glide formation and vowel deletion, the two additional speakers do not follow the rates of the main speaker as closely as might be expected. This comparison is given in Table 18 below, but is based on so few tokens for the second and third speakers that it is simply meant as an illustration of the potential discrepancies between speakers.

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Table 18. Three-way comparison of output rates for three most frequent forms speaker I M C /Ϫa/ [Ϫ] 42% [Ϫ⍝a] 34% 33.3% [Ϫa] 16% 67.7% [a] 8% 100% N 50 3 2 /ea/ [a] 31.4% 25% [e] 25.7% [e⍝a] 25.7% 25% 100% [ea] 17.1% 50% N 35 4 1 /ia/ [ja] 56.3% 20% [a] 25% [ia] 12.5% 60% [i] 3.1% 100% [ija] 3.1% 20% N 32 5 1

It is important to bear in mind that the comparison numbers are so small that they should not be used to conclude that the speakers’ grammars differ in any fundamental way. In fact this situation suggests that speakers with very similar grammars may have meaningful differences in output rates. Because the runs with OTSoft are meant to provide a model of a grammar in general, this further suggests that tolerating relatively high error rates is appropriate.

4.2.6 Structured variation

The main observation from this result, however, is that it seems clear that the choice of whether or not to delete a vowel is anything but random. The hierarchy of constraints in Table 17 gives us a hierarchy of how easily a vowel deletes, with vowels at the top being the most resilient. A quick glance suggests that the vowels are patterning according to at least three features phonology has traditionally appealed to. The top of the hierarchy is made up of round vowels and nasal vowels: being [round] or [nasal] seems to protect a vowel from deletion. The front unrounded oral vowels are all near the bottom of the hierarchy: [back] may also have this protective effect. Strikingly, only the high front round vowels [y] and [௙] don’t pattern as they might be expected to according to these three simple factors. Finally, while no clear effect of height features jumps out from the table, there also isn’t conclusive evidence that no such effects exist.

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4.2.7 Criteria for best constraint set

This preliminary run serves as an excellent basis for determining which set of constraints best accounts for the vowel deletion data. The best run will be one whose rate for average error per candidate minimally exceeds that of the preliminary run. Also, for a run to be successful, the constraint set must not result in any of the candidates that sometimes win being harmonically bounded by candidates that never win (i.e. a sometimes winner must not have a superset of the constraint violations of an always loser). In the GLA as implemented in OTSoft, a sign of harmonic bounding is that a constraint’s ranking value continually decreases towards negative infinity. When this takes place, this means that the set of constraints being tested cannot be considered the best model for the vowel deletion data: only ‘clean’ runs are under consideration.

Finally and most importantly, the preliminary run suggests that the feature-based approach used to analyze the coalescence data (section 3.1.6) is promising. The kinds of features used for coalescence seem to fit well with what at a glance appears to be constraining whether or not a vowel deletes. For this reason, the sets of constraints that were tested are based on MAX [F] constraints, which penalize the deletion of features in ways that are discussed in section 4.3.1 below. Because of the requirements of OTSoft it was not possible to automate the process of determining the optimal run. However this was greatly simplified with the use of the Input Formatting Utility for OTSoft (Scott & St-Amand 2010), which formatted Excel files according to specified parameters.

4.3 Best Constraint Set

The best model for the vowel deletion stratum, as determined by the GLA in OTSoft, is given in Table 19. Figure 23 below it provides the relevant constraint formulations. Appendix G lists the frequencies of candidates in the data compared with the frequencies generated by OTSoft for each underlying form.

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Table 19. Best constraint set for vowel deletion Constraint Ranking Value DEP 103.198 HIGHEST MAX [nasal] 102.367 RANKING NODIPHTHONG 99.627 *VV 99.622 MAX [back] 99.029 MAX V1 98.780 MAX WORD INITIAL 98.771 MAX ROOT NODE 98.592 LOWEST V=N UCLEUS 98.588 RANKING MAX [round] 33.733 average error per candidate: 15.799 cycles: 100 000 learning trials: 1 000 000

Figure 23. Constraint formulations for best constraint set for vowel deletion

DEP : Every segment in the output must correspond to a segment in the input. MAX [F]: Every feature in the input must correspond to a feature in the output. NODIPHTHONG : A nucleus must not be made up of two vowels. *VV: Two vowels must not appear in a row. MAX V1: The first of two vowels in a row in the input must correspond to a vowel in the output. MAX WORD INITIAL : Every word-initial vowel in the input must correspond to a vowel in the output. MAX ROOT NODE : Every segment in the input must correspond to a segment in the output. V=N UCLEUS : Every segment corresponding to a vowel in the input must make up a unique nucleus.

This constraint set emerged as best based on a few main criteria. Firstly, unlike many other possible constraint sets, the run was clean meaning there was no evidence of harmonic bounding (Appendix H provides a summary of the unsuccessful grammars submitted to OTSoft: for clean runs, the average error rate per candidate is reported; otherwise, the grammar is described as invalid). Moreover, the best constraint set had the lowest average error per candidate of any constraint ranking that both fit with the coalescence data and was judged to plausibly correspond to a speaker’s grammar. With respect to coalescence, some runs with binary features did very slightly better (improving the average error per candidate by 0.013 to 0.083%), but this improvement was judged insufficient to warrant adopting one of these grammars given their incompatibility with the cases of coalescence. In terms of plausibility, none of the runs had an error rate as low as the preliminary run in Table 17 which contained MAX [V] constraints. However, a grammar in which each individual vowel was a primitive that constraints operated over, with the implied absence of phonological generalization and abstraction, was not considered a possible grammar.

The constraint set is very limited: it contains only three MAX [F] constraints, as well as the additional constraints described in 4.2.2. The three MAX [F] constraints that make for the best run

108 are precisely the ones that would expected according to the preliminary run: having the features [nasal], [back] and/or [round] does indeed significantly affect the probability that a vowel will delete. The fact that vowel deletion operates over abstract entities such as features reinforces its status as a phonological, rather than phonetic, process (see 3.1.2.3.1). The interpretation of the

MAX [F] constraints requires an important clarification, which is discussed in 4.3.1 below. Finally, the results are compatible with the feature hierarchy introduced in 3.1.6, as discussed in 4.3.2 below.

4.3.1 MAX [F] constraints: strong vs. weak

One factor that turned out to be important in determining a best run was the way violations to

MAX [F] constraints were assigned. Two possibilities, both of which seem legitimate, exist. One approach is to simply assign a violation to every constraint corresponding to the deleted vowel’s features. For instance, the output [ Ϫ] corresponding to underlying / iϪ/ would violate MAX [ATR] and MAX [high], assuming a set of feature specifications in which [ i] has both those features and no others. While this approach is straightforward and seems unproblematic, it was troubling that the deletion of [ i] from a / ii/ sequence should result in identical constraint violations as its deletion from / iϪ/. In the first case, the resulting vowel has the features targeted by the constraints, and so it seems incorrect to claim they have been deleted. Intuitively, turning / ii/ into

[i] may involve the violation of some faithfulness constraint, but seems to represent a much less serious breach of faithfulness than turning / iϪ/ into [ i]. For this reason, a second approach to

MAX [F] constraints was tested according to which the deletion of a vowel caused a violation of a constraint only if the vowel contained a feature not present in the output. Under this view and assuming the same feature specifications as above, mapping / iϪ/ to [ Ϫ] would result in a violation of MAX [ATR] and MAX [high], while mapping / ie/ to [ e] would only incur a violation of MAX

[high] given that the input [ e] has the feature [ATR], and mapping / ii/ to [ i] would not violate any MAX [F] constraints. This second approach to MAX [F] constraint violations has looser requirements than the first: the first may be referred to as ‘strong’ and the second ‘weak’. Figure

24 gives the formulations of the strong and weak versions of MAX [F].

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Figure 24. Strong and weak MAX [F]

STRONG MAX [F]: Every unique feature in the input must correspond to a unique feature in the output. WEAK MAX [F]: Every feature in the input must correspond to a feature in the output.

A comparison of weak and strong MAX [F] constraints revealed that the weak versions were a better fit for the vowel deletion data. The strong constraints more often lead to runs that weren’t clean (i.e. involved harmonic bounding), but more importantly, in test runs based on small sub- samples, the weak constraints fit the data better, as determined by the average error per candidate. For this reason, the MAX [F] constraints in the best constraint set in Table 19 involve this weak interpretation: a candidate for instance incurs a violation of MAX [nasal] if the input contains the feature [nasal] and the output does not. Because as mentioned, the weak constraints mean that the mapping of / ii/ to [ i] does not violate any MAX [F] constraints, the constraint

MAX ROOT NODE was introduced to penalize this type of form. The deletion of a nucleus position in a syllable, be it through vowel deletion or diphthong or glide formation, incurs a violation of this constraint. The other constraints are as described for the preliminary run in section 4.2.2 above.

4.3.2 Feature Hierarchy

The three necessary features, [back], [round] and [nasal], are assigned in a way that is compatible with the contrastive hierarchy discussed in section 3.1.6.6 and repeated below as Figure 25 again divided into two parts according to the initial [ATR] split.

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Figure 25. Contrastive hierarchy for best constraint set

feature hierarchy: [ATR] > [high] > [low] > [round] > [back] > [nasal]

[ATR]

[high] (–high)

[round] (-round) [round] (-round) i e

[back] (–back) [back] (–back) u y o ø

(–ATR)

[low] (–low)

[round] (-round)

[back] (–back) [back] (–back) Ϫ

[nas] (–nas) [nas] (–nas) [nas] (–nas) [nas] (–nas) a a ѐ ѐ œ œ ϯ ϯ

As discussed in the coalescence section and above, feature specifications corresponding to other contrastive hierarchies were also tested on the vowel deletion data, either because they were marginally better for coalescence, or because they seemed plausible or natural. 41 No other

41 Contrastive hierarchies resulting in [ œ] not bearing any features were for instance tested, on the assumption that it might be desirable to have the default epenthetic vowel be unmarked (see Rice 2007). These hierarchies did not fit with the coalescence data or improve the account for vowel deletion, and were therefore not selected. Having the epenthetic vowel bear a contrastive

111 hierarchy did as well, either with the successful set of constraints, or with any of the other sets of constraints discussed in 4.3.3 below: see Appendix H.

One of the most striking characteristics of the selected constraint set is that it is compatible with the Contrastivist Hypothesis (see section 3.1.6.1): only contrastive features are active at this level of the phonology as well. This is the constraint set that will account for the variable data within the complete model in Chapter 5. Despite the complexity of some aspects of the analysis, the component having to do with features is actually relatively simple. Compared with what could have been posited, the choice of which phonological material a learner has to attend to is very constrained. Because this aspect must vary between languages, unlike the overall architecture of the grammar, its relative simplicity is a benefit.

4.3.3 Constraints that were not selected

Most of the less successful and downright unsuccessful runs involved modifying the MAX [F] constraints. While the selected run is compatible with the view that features are privative (though it could of course be that MAX [–F] constraints are also present but ranked very low), other types of constraint sets were also tested. A set with MAX [+F] and MAX [–F] constraints was tested, both under the assumption that vowels were fully specified (as in Table 10 from Chapter 3) and that they had bivalent values that were assigned contrastively. For this second case, while a number of hierarchies were put to the test, Table 20 shows the set that would have been used for the hierarchy in Figure 25 above for illustration’s sake.

feature is not necessarily a problem: de Lacy (2002) argues that the choice of epenthetic vowel does not correlate with unmarked status.

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Table 20. Bivalent feature set for successful hierarchy feature hierarchy: [ATR] > [high] > [low] > [round] > [back] > [nasal] i y e ø ϯ ϯ œ œ a a Ϫ ѐ ѐ o u [ATR] + + + + – – – – – – – – – + + [back] – – – – – – + + + + + [high] + + – – – + [low] – – – – + + + – – [nasal] – + – + – + – + [round] – + – + – – + + + + + +

As discussed above, and summarized in Appendix H, these additional MAX constraints did not result in a sufficiently better account of vowel deletion to warrant the weakening of the approach to coalescence.

Runs that did and did not include MAX ROOT NODE , MAX WORD INITIAL and MAX V1 were also compared. Following Casali’s (1997, 1998) work (which served as the motivation for

MAX WORD INITIAL ), the data were also coded to account for two other cross-linguistic tendencies: the preservation of monosegmental morphemes and of vowels in lexical morphemes, all else being equal. The input forms were separated according to whether V1 and V2 are monosegmental and/or lexical, and violations of the constraints MAX MONO and MAX LEX were assigned to deleted vowels accordingly. Neither of these constraints appeared to contribute positively to accounting for the data. In small test runs, their inclusion did not lower the average error per candidate, nor did the constraints themselves get assigned high-ranking values. If OTSoft had ranked the constraints highly, this would have suggested that there was indeed an effect prohibiting the deletion of vowels in monosegmental and/or lexical morphemes, but no such effect comes through. When they are added to the successful constraint set, the resulting runs are not clean (pointing to harmonic bounding) which means a direct comparison may not be possible, but it is worth noting here too that there isn’t an improvement to the average error per candidate, and that the constraints themselves are ranked quite low.

4.4 Summary

This chapter focused on how best to model the extensive variation within the QF data. The most robust and complex of this variation has to do with the patterns of vowel deletion. In particular, when two vowels are in hiatus context, there are many potential outcomes: deletion of the first vowel, deletion of the second vowel, hiatus, gliding/diphthongization and glide insertion. In

113 reviewing a variety of proposals for treating this variation, I argued that a stochastic constraint- based approach was best able to account for these outcomes. On this basis, the rest of the chapter was devoted to arriving at a model for the portion of the grammar where vowel deletion takes place, using Stochastic OT implemented through OTSoft. Although optionality and variability are pervasive throughout the phonological system of QF, the situation is not chaotic. Indeed, the variation in the vowel deletion data is structured, in that rates of deletion are sensitive to phonological features. Moreover, the data fits with the hypothesis that only contrastive features, which are assigned hierarchically, are active in the phonology. To arrive at an account of the variability, a number of constraint sets and rankings were considered. The best of these was identified and will form a part of the overall model proposed in Chapter 5. As we will see, I argue that a grammar with multiple levels is required: the constraints from this chapter will make up the last level.

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Chapter 5 Opacity

A number of processes in the data (assibilation, liaison and h-aspiré) involve generalizations that are either not surface apparent or not surface true, and so can be considered opaque. The case of opacity that is perhaps the least controversial has to do with the intersection of assibilation and vowel deletion. As discussed inChapter 3 and Chapter 4, the dental stops / t, d/ become / ts, dz/ before high front vowels and glides. But there are a number of cases in the data where this assibilation takes place even though the high vowel trigger has deleted, as in 106 and 107.

106. Je l’ai pas dit à personne là de c’te- m- mon groupe là. [dza] (=/ di#a/) ‘I didn’t tell anyone from that- m- my group, there’ (I/11/6)

107. Bon, tu t’es-tu ennuyée de moi? [tsanіije] (=/ ty#anіie/) ‘So, did you miss me?’ (I/14/20)

Assuming for the time being that the generalization that assibilated stops appear before high front vowels is a good one, the examples in 106 and 107 involve opacity because there are assibilated stops where there should not be (see 5.1 for a formal definition of opacity). The categorization of a process as opaque depends on how it is analyzed (5.2.1 presents a formalization of assibilation), which is an important caveat with respect to the findings of this chapter. However, given on the one hand the existence of processes that quite clearly seem to be opaque and on the other hand the fact that many opaque phenomena show up in the data, opacity is clearly a crucial issue to the analysis of hiatus in Québécois French (QF). This means that a successful account of hiatus and hiatus resolution in QF must be able to handle opacity: section 5.1 reviews previous accounts of opacity, 5.2 goes over the cases of opacity, 5.3 presents the proposed solution, 5.4 gives the details of each level, and 5.5 shows how the model applies to the data.

5.1 Opacity before and after the era of Optimality Theory

The way opacity is typically conceived of is a legacy of rule-based phonology, which was well equipped for dealing with it. As soon as two rules are applicable to a single form, it is possible that their effects might interact. One rule may for instance create just the situation where the

115 other rule would apply, or conversely one rule might change a context in such a way that the other rule would no longer apply. This may lead to situations where a surface form seems to have violated the rules used to derive it. Kiparsky (1982a) formalized this situation as in 108 below.

108. A rule R of the form α→β / γ__ δ is opaque if there are surface representations in the language having either (i) α in the environment γ__ δ (underapplication) or (ii) β derived by R in an environment other than γ__ δ (overapplication) (Kiparsky 1982a: 75)

Returning to the interaction between vowel deletion and assibilation, and assuming the simplest possible rule-based analysis (as in 109), the surface form in 106 where [ dza] surfaces from / di#a/ clearly fits Kiparsky’s criteria.

109. A rule for assibilation: d→dz / __ i

Here [ dz] is derived by the assibilation rule in an environment other than / __ i, and thus the example involves the overapplication of the rule.

The phonologies of the world’s languages are replete with examples of interaction between rules, which offer support to the natural solution within rule-based phonology, namely rule ordering. Rule ordering could explain both the underapplication of a rule (that is, why a phonological context that typically causes a given rule to apply could fail to do so), as well as the overapplication of a rule (that is, why a rule has applied to a form that does not have the right context). Underapplication of rules can be dealt with by ordering the apparently ignored rule before a rule creating the context that the apparently ignored rule would have needed, explaining why the latter fails to apply. Overapplication can be dealt with by ordering the rule that does apply first, and then having another rule change the phonological context afterwards. Given the wide range of data that seem to exemplify apparent under- and overapplication of rules (see for instance Odden 2005: Chapter 5; Gussenhoven & Jacobs: Chapter 6), many considered that rules could be ordered in any logically possible way, with for instance three rules having six possible orderings. 42

42 Notwithstanding debates about intrinsic rule ordering (see for instance Koutsoudas, Sanders & Noll 1974).

116

Because problems related to opacity were naturally solved within rule-based approaches, opacity was not considered a hurdle to phonological analysis. Instead, its presence was expected and served as support for the framework. The advent of OT (Prince and Smolensky 1993/2004) marked a significant change with respect to how opacity is handled. It brought with it some important advantages through its use of violable constraints and parallel evaluation: of particular importance here is OT’s ability to model variation (see Chapter 4). But from the beginning it was clear that a persistent thorn in its side would involve processes that were either not surface apparent or not surface true. Early attempts to handle opacity, such as Sympathy Theory (McCarthy 1999), Output-output constraints (Benua 1997), fought to preserve the single-level architecture of traditional OT. Such approaches suffer from serious problems, both from the point of view of descriptive adequacy and in more fundamental conceptual ways (as in particular Bermúdez-Otero (2003) argues forcefully). No proposal relying on classical single-level OT has been widely held to succeed with data exhibiting opacity, which has contributed to an opening for multi-level analyses. These have the benefits of rule-based approaches in that their serial architecture can account for opaque processes in a very natural fashion, while gaining most of the benefits of OT. These frameworks are discussed in greater detail in section 5.3.

Although there is not consensus about how opacity ought to be formalized within constraint- based models, Bermúdez-Otero (2004) gives what seems to be a good translation of Kiparsky’s formulation. He defines under- and overapplication, as in 110 below.

110. Underapplication: a language has grammatical output forms containing [ γαδ ], yet there is independent evidence requiring the ranking * γαδ » FAITH-α.

Overapplication: a language has expressions where input / α/ is unfaithfully mapped onto output [ β], yet there is no markedness constraint M ranked above FAITH-α such that the mapping / α/→[β] in these expressions increases harmony with respect to M (Bermúdez-Otero 2004: 2)

As we will see in section 5.2, this formulation successfully captures the various kinds of opacity just as Kiparsky’s does in a rule-based analysis.

5.2 Opaque processes in the data

Three main processes in the data exhibit opacity, and will be reviewed in this section. 5.2.1 deals with assibilation, 5.2.2 liaison, and 5.2.3 h-aspiré. Each section will provide an overview of the

117 data, a formulation of the constraints that are involved, and a description of how Bermúdez- Otero’s criteria from 110 are met.

5.2.1 Assibilation

As discussed above, there are many tokens where the affricates [ ts, dz] appear despite the absence on the surface of the expected high front vowels. Of the fifteen cases where a high front vowel deletes between a dental stop and a vowel that is not high and front, all but one have an assibilated stop before the normally non-triggering vowel, as in 111 and 112.

111. T’en veux-tu un grilled cheese ou non? [tsœ] (=/ ty#œ/) ‘Do you want a grilled cheese, or no’ (I/21/21)

112. Bon, tu t’es-tu ennuyée de moi? [tsanіije] (=/ ty#anіie/) ‘So, did you miss me?’ (I/14/20)

As argued in section 3.1.2, the high vowels are truly deleted and not simply devoiced. With respect to assibilation, it is usually described as categorical within the word in QF (occurring before any high front vowel or glide and never occurring in any other context that does not have deletion) and optional across a word boundary (Walker 1984, Côté 2005b). This optionality undermines any approach that would wish to place assibilation outside of the active phonology. One might attempt to circumvent the opacity problem by claiming that assibilation is somehow provided in underlying forms, 43 but this would lead to a very fractured analysis given that assibilation across word boundaries has to be in the active phonology, meaning that assibilation within and across words would end up receiving two completely different explanations. For these reasons, it seems clear that these cases involve the two phonological processes of vowel deletion and assibilation, but that the motivation for the latter is not apparent given the former. This kind of word-internal assibilation is usually described as categorical, but it is worth noting that the one

43 This is an undesirable approach given the redundancy of the information as well as the fact that the assibilated stops would not appear in any other context in an underlying form, but seems like the only remaining move to claim that these forms are not opaque.

118 case where it fails to apply before a deleted high vowel is not unique in the data. Of hundreds and hundreds of cases before a high front vowel, there is the odd form where a plain dental stop appears. While it is likely best to think of word-internal assibilation as categorical, it is more accurate to describe it as very nearly categorical.

As mentioned above, assibilation can also take place across word boundaries, although it is reported to be optional rather than obligatory (Walker 1984, Côté 2005b). This characterization seems correct, but the data contain no tokens that would allow it to be put to the test. Of the cases where a high front vowel follows a fixed final consonant (such tokens were only identified in the first stage of extraction), none involve a dental consonant. Nonetheless, the fact that assibilation may or may not take place between words should be accounted for.

Kim (2001) provides an analysis of assibilation based on data from a number of languages including QF. She proposes that assibilation is the result of the insertion of a feature [+strident] into [ t] or [ d]. This insertion results from the presence of a high (and front, in the case of QF) vowel or glide, but the feature does not come from this trigger. Instead, it is the result of a reanalysis of the turbulence that often intervenes between a stop and a high vowel or glide during the articulation of such sequences. Her claim is that this phonetic fact is phonologized as the insertion of [+strident] whenever the environment that is associated with turbulence occurs. This proposal is schematized in Telfer (2006), by modifying a diagram of Kim’s (2001), as in Figure 26 below.

Figure 26. Kim’s (2001) phonetically based account of assibilation

(Telfer 2006, 81: modified from Kim 2001, 102)

Kim’s analysis uses bivalent features, with a [+high] feature on [ i] triggering the insertion of

[+strident], which in turn causes [–strident] to delink from [ t]. I have argued that QF vowels bear

119 privative features, and assume that the same would be true of consonants. It is not within the scope of the thesis to settle on a contrastive hierarchy and set of feature specifications for QF consonants, but it seems safe to assume that Kim’s proposal could be minimally modified to allow for privative features. In this view, the feature [high] on the high front vowels would trigger the insertion of the feature [strident] on the stops, which would cause them to become affricates. A simple markedness constraint, as in 113 below, can capture the fact that a [high] segment must be accompanied by a [strident] segment.

113. [strident]/_[high]: A segment with the feature [high] must be preceded by a segment with the feature [strident]

The constraint in 113 dominates a faithfulness constraint barring the insertion of [strident], which can be formulated as the general anti-epenthesis constraint DEP (No insertion). A complete analysis of assibilation requires constraints that prohibit other stops from bearing the feature [strident] and constraints that prohibit the back high vowel and glide from triggering insertion of [strident]. A more complete account including such constraints is provided in 5.4.1, but for the purposes of this discussion, the ranking in 114 is sufficient.

114. A constraint ranking for assibilation: [strident]/_[high] >> DEP

Given this ranking, forms with an assibilated stop in the absence of a high vowel, as in 111 (repeated below as 116) and 112, fit Bermúdez-Otero’s formulation of overapplication, which is repeated below.

115. Overapplication: a language has expressions where input / α/ is unfaithfully mapped onto output [ β], yet there is no markedness constraint M ranked above FAITH-α such that the mapping / α/→[β] in these expressions increases harmony with respect to M (Bermúdez-Otero 2004: 2)

116. T’en veux-tu un grilled cheese ou non? [tsœ] (=/ ty#œ/) ‘Do you want a grilled cheese, or no’ (I/21/21)

In this example, input / t/ is unfaithfully mapped onto output [ ts], but there is no markedness constraint ranked above DEP such that this mapping increases harmony with respect to this markedness constraint. The markedness constraint that is responsible for assibilation does not apply here given the absence of the triggering [high] feature in the output form. This means that

120 these types of cases where assibilation and vowel deletion interact fit perfectly with Bermúdez- Otero’s definition of opacity by overapplication.

5.2.2 Liaison

As previously discussed, in Chapter 2 in particular, there are cases where a liaison consonant appears despite the fact that at the surface it does not serve to separate two vowels. In fact there are more than fifty tokens like those in 117 and 118.

117. Pis il a dit, euh, elle nous a dit de parler de la Renault Twingo. [nzϪ] (=/ nu#Ϫ/) ‘And he said, uhm, she told us to talk about the Renault Twingo’ (I/23/49)

118. Bien, tu sais, c’est ordinaire là, quand même. [stѐలdznϯల] (=/ s#e#ѐrdinϯr/) ‘Well, you know, it’s ordinary, still’ (I/41/14)

The only exceptions to this pattern involve the pronoun subject on ‘we’ appearing before a verb that has lost its first vowel. Where we might expect the liaison consonant [ n] to surface, it does not.

Viewing forms such as 117 and 118 as exemplifying opacity requires adopting the analysis put forward in Chapter 2 that this type of liaison is functionally motivated by hiatus avoidance. While a small set of liaison consonants can best be seen as underlying, following Côté’s (2005a) proposal, I argue that these cases involve an epenthetic consonant. I attribute this epenthesis to the constraint *VV (No hiatus) dominating the constraint DEP (No epenthesis). But in the examples here, epenthesis occurs in violation of DEP despite not resulting in increased harmony with respect to a higher ranked markedness constraint. The markedness constraint *VV is not involved in this case because the outputs would not have had hiatus even without the inserted liaison consonant. Just as was the case for assibilation, this is a case of overapplication as defined by Bermúdez-Otero.

There is another striking resemblance between liaison and assibilation. Both processes are obligatory in certain restricted domains and optional in larger ones. As discussed for liaison (see section 2.3), there are some contexts, such as between a determiner and a noun and a clitic and a verb, which are basically categorical. These contrast starkly with the only robust pocket of variability in the liaison data, between the present tense forms of the verb être ‘to be’ and its

121 complements. In this context, it is about as likely for a liaison consonant to surface as not. The asymmetry within both liaison and assibilation combined with the similarities between these processes should be captured.

5.2.3 H-aspiré

The analysis of h-aspiré advocated for in section 3.2.2 and 3.2.3 intrinsically involves opacity. The correct behaviour of forms such as 119, 120 and 121 is explained through the presence of an abstract segment / h/ that is absent from the surface.

119. Je me rappelle, on était en haut , p’is elle a [...] [ao] (=/ a#ho/) ‘I remember, we were upstairs, and she’ (I/24/9)

120. Dans le homard aussi. 44 [lѐomaల] (=/ l#homaల/) ‘In the lobster too.’ (I/42/24)

121. Si tu peux être sauvée grâce à mon x [...] (/ksگks] (=/ mѐ#hگmѐ] ‘If you can be saved thanks to my (symbol) x’ (I/31/5)

This abstract segment results in anomalous behaviour with respect to two processes, liaison in 119 and 121 and schwa insertion in 120. In the case of liaison, consonant epenthesis does not occur, even though the vowel-vowel sequence that should have triggered it is present at the surface. This fits in with Bermúdez-Otero’s formulation of underapplication, repeated below.

122. Underapplication: a language has grammatical output forms containing [ γαδ ], yet there is independent evidence requiring the ranking * γαδ » FAITH-α. (Bermúdez-Otero 2004: 2)

As we saw in 5.2.2 above, normal cases of liaison require the ranking *VV >> DEP . These cases therefore fit the definition in that there are [VV] outputs, yet there is independent evidence ranking *VV above the relevant faithfulness constraint. In the case of schwa insertion (as in 120), it takes place in a context where it would not be expected, preceding a vowel. This is a case

44 The quality of V1, which would be expected to be [œ], is most likely due to backness assimilation to V2.

122 of overapplication, in that the mapping of /V/ to [œV] does not result in increased harmony with respect to any markedness constraint. The h-aspiré data therefore involves two kinds of opacity, with the epenthesis of schwa and liaison consonants interacting with the deletion of the abstract segment / h/.

5.3 A stochastic multi-stratal grammar

As discussed in 5.1, the best way to handle opacity within OT is to keep the serialism of earlier derivational approaches. Two proposals that do this are Rubach’s Derivational Optimality Theory (2000) and Kiparsky’s Lexical Phonology and Morphology OT (2000) or Stratal OT (2010). 45 For these frameworks, there are three distinct constraint levels, corresponding to the classic Lexical Phonology (Kiparsky 1982b, among others) stem, word and postlexical levels.

Such an approach is very appealing. As we saw in Chapter 4, OT is much better suited to dealing with variation than traditional rule-based approaches. While modeling variation is not the intention of Kiparsky and Rubach’s approaches, the fact that they are OT-based seems a definite plus. Moreover, they handle opacity in a satisfactory way that has intuitive appeal. Finally, because the three constraint levels correspond to clear distinctions within the grammar, the added burden of serialism seems quite containable.

Unfortunately, an analysis with three constraint levels corresponding to stem, word and postlexical levels cannot work for the QF data here. Firstly, the extensive variability requires mechanisms that are absent from these models. However, assuming that this problem can be circumvented, the more important problem is that the QF opacity does not map to the three proposed levels. This can be seen clearly in the case of liaison. As discussed in section 5.2.2, liaison and vowel deletion interact in a way that can be said to involve opacity. Using a model with multiple levels, vowel deletion would have to apply in a level subsequent to the level in which liaison applies. While there is no problem ascribing vowel deletion to the postlexical level, liaison cannot be said to apply at the stem or word level. Liaison applies across words, including in some contexts where the two words do not even belong to the same clitic group. The domain of application of liaison can best be thought of as the phonological phrase (Walker 2001: 161),

45 McCarthy’s harmonic serialism (2010) is also a proposal in this direction.

123 which would have to map to the postlexical level as well. With vowel deletion and liaison on the same level, we lose the possibility of accounting for opacity. As we will see, this situation holds for many of the interacting processes here.

One possible solution to this is to simply stipulate that there are exactly as many levels of constraints as the data require, perhaps as few as two. However this loses one main strongpoint of the framework, which is that the division between levels is not arbitrary but instead dictated by an aspect of the phonology that is available to learners. An avenue that seems preferable is to claim that the basic insights of Rubach and Kiparsky are correct but that different languages may map different prosodic constituents to each level of the grammar. More specifically, it may be that for some languages the stem does not define a domain of phonological activity. 46 This would mean that learners would have to determine which of a small number of constituents each level targets. In addition to stem and word constraint levels, we can add possible levels corresponding to the fairly standard prosodic constituents proposed by Nespor & Vogel (1986), namely the clitic group, the phonological phrase, the intonational phrase and the utterance. While this is not quite as simple as claiming that there are three universal constraint levels, it adds less complexity than claiming that languages can define any number of levels in any way.

For the QF data here, we can define three levels, corresponding to the clitic group (5.4.1 below), the phonological phrase (5.4.2) and the intonational phrase (5.4.3). It may seem strange, especially in comparison with typical analyses of English phonology, to rely on such large prosodic constituents. However, it has long been established that many if not most French phonological processes make reference to the phonological phrase. Walker (2001) describes the phonological phrase as “perhaps [the] most fundamental unit in French phonology” (31) and cites stress, vowel length, syllabification, schwa and intonation as requiring reference to phrasal boundaries. Because of this, it seems appropriate for the phonological phrase to correspond to the middle stratum, as the word level would in analyses of English.

46 Similarly, for instance, Dresher (1983) shows that Tiberian Hebrew is sensitive to prosodic constituency including and above the phonological word.

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5.4 Defining the levels

5.4.1 Level 1: Clitic Group

The smallest prosodic unit that must be referred to in analyzing hiatus and hiatus resolution in QF is the clitic group. This constituent is situated between the phonological word and the phonological phrase in the prosodic hierarchy (Nespor & Vogel 1986, Hayes 1989). It serves to account for the behaviour of clitics, which are phonologically dependent on the word that comes either before or after them (Walker 2001: 31). We have seen a number of items in the data that are phonologically dependent on the word that hosts them, especially in the context of discussing the absence of schwa (see 2.2) and of discussing liaison with pronouns and determiners (see 2.3). For example, both bold portions of 123 below contain a clitic (the first has a subject pronoun and the second a definite determiner) and constitute a clitic group.

123. [...] c’est bien le fun . [se] [ lfѐn] (=/ s#e/, / l#fѐn/) ‘it’s really fun’ (I/21/n.t.)

Following the Strict Layer Hypothesis (Selkirk 1984), I assume that every phonological word also forms a clitic group, whether or not the latter includes a clitic. In the example above, this also makes bien [bϯ] ‘really’ a clitic group, meaning that the excerpt contains three clitic groups.

As we have seen, liaison is virtually obligatory within this context, as is assibilation. These facts set the stage for the basic description of the first level. It is the simplest of the levels in that it does not stray in any way from traditional OT. It contains a set of constraints in a strict hierarchy: while there is not evidence for every ranking, it is assumed that every constraint strictly dominates or is strictly dominated by every other constraint.

As described in Chapter 2, the basic facts of liaison require an anti-hiatus constraint *VV to be ranked high in the hierarchy. To allow for epenthesis as a repair strategy, this constraint must dominate DEP , a general constraint prohibiting the output from containing any material not present in the input. A general constraint prohibiting the deletion of any material, MAX , must also dominate DEP in order to block hiatus from being resolved through deletion. In fact, as we

125

47 will see, no deletion at all takes place on this level, meaning that MAX can be placed at the very top of the hierarchy. Turning now to h-aspiré, the constraint * h↔C was introduced in section 3.2.3 to block the insertion of liaison consonants and motivate schwa epenthesis. Given the latter, this constraint must dominate both DEP and *VV. The abstract segment / h/ that separates other phonetically vowel-initial words from h-aspiré ones remains in the output of level 1. For this reason, many h-aspiré forms will emerge from the first constraint level having remained completely faithful to their underlying form. Finally, as discussed in 5.2.1, I adopt and slightly modify the analysis of Kim (2001) and attribute assibilation to the insertion of the feature [strident]. This feature must be present preceding any segment bearing the feature [high] with the exception of [back] segments. This will be implemented by having a constraint that prohibits a [strident][back] sequence dominate a constraint that imposes a [strident][high] sequence. I assume that (unshown) high-ranking constraints stop non-coronal segments from bearing [strident], as this would lead to illicit segments, and stop any [strident] fricatives from deleting that feature in the context of a high vowel, either because the resulting segment would be illicit or else would constitute too serious a violation of an IDENT constraint. Figure 27 provides the constraint formulations for the Clitic Group constraint set and Figure 28 the constraint ranking.

Figure 27. Constraint formulations for Level 1: Clitic Group

*VV: Two vowels must not appear in a row. MAX : Every feature/mora in the input must correspond to a unique feature/mora in the output. DEP : Every feature/mora in the output must correspond to a feature/mora in the input. *h↔C: The segment / h/ must not be adjacent to a consonant. *[strident][back]: The features [strident] and [back] must not appear on two segments in a row. [strident]/_[high]: A segment with the feature [high] must be preceded by a segment with the feature [strident]

47 Multi-level analyses of languages other than QF have similarly found deletion to be a feature of later levels (e.g. Anttila et al. 2008).

126

Figure 28. Constraint ranking for Level 1: Clitic Group

MAX | *h↔C *[strident][back] | *VV [strident]/_[high] | DEP

5.4.2 Level 2: Phonological Phrase

The second constraint level targets the constituent that directly dominates the clitic group, the phonological phrase. This constituent can be identified on phonological grounds, although it has syntactic correlates as well. Identifying phonological phrases with certainty is complex (see 3.3 in Walker 2001), but for our purposes a few phonological criteria are sufficient. 48 As a general rule, phonological phrases occur between pauses or potential pauses, have final stress, and have fewer than six syllables (Walker 2001: 32). The example from above that had three clitic phrases (123 repeated below as 124) makes up a single phonological phrase.

124. [...] c’est bien le fun . [se bϯ lfѐn] (=/ s#e#bϯ#l#fѐn/) ‘it’s really fun’ (I/21/n.t.)

The structure of this constituent is given in Figure 29 below.

48 As discussed in 5.3, the place of the phonological phrase in French phonology is well established. Moreover, the only aspect of the analysis that crucially depends on identifying phonological phrases in the data has to do with the optionality of liaison, which is both well attested (see 2.3) and illustrated with data in 5.4.2.1 below and elsewhere in the thesis.

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Figure 29. Example of prosodic structure

IPh

PPh

ClG ClG ClG

PWd PWd PWd PWd PWd c’ est bien le fun

The lowest level shows phonological words (PWd), which come together to create clitic groups (ClG), which in turn form the phonological phrase (PPh). The figure also shows the largest prosodic group to define intonational patterns, the intonational phrase (IPh: see 5.4.3).

5.4.2.1 Optional processes in level 2

As we saw, both liaison and assibilation can apply across separate clitic groups, meaning that both processes must be possible within a larger domain. Moreover, as previously discussed, both of these processes are optional within this larger domain, as 125-128 illustrate for liaison.

125. En plus, moi, c’est elle qu’il m’avait nommé [...] [se⍝ϯl] (=/ s#e#ϯl/) ‘plus, (to me,) it’s her that he had named’ (I/21/16)

126. Je suis une vraie dinde, hein? [Ѐ௙n] (=/ Ћ#sіi#yn/) ‘I’m a real turkey, eh?’ (I/47/11)

127. C’est aussi cruel de mettre un homard vivant dans de l’eau chaude. [stosi] (=/ s#e#osi/) ‘It’s as cruel putting a live lobster in hot water.’ (I/42/20)

128. [...] pis moi aussi, je suis un peu comme ça [...] [Ѐtœ] (=/ Ћ#sіi#œ) ‘and me too, I’m a little like that’ (I/4/12)

In the first two of these examples, no liaison consonant surfaces, while the second pair have [ t]. In Chapter 2, this optionality was preliminarily arrived at by having a set of *VV constraints that applied to different prosodic constituents. Now that we have established the need for three constraint levels, we can return to a single *VV constraint, but have this constraint operate in

128 more than one level. To model optionality, we need a simple mechanism that produces two different outputs at the same frequency. For this, Anttila’s proposal for crucially unranked constraints (see section 4.1.2) works very well. For the liaison examples above (125-128), this involves placing the constraint that militates for liaison (*VV) as crucially unranked with the constraint that militates against it (DEP ). This would mean that half the time the grammar was evaluated, the constraint ranking would be DEP >> *VV, and the other half it would be *VV >>

DEP . The first scenario would produce the forms in 125 and 126 without liaison, and the second those in 127 and 128 with liaison. Therefore on this level, the constraints that motivate liaison consonant epenthesis and assibilation will be crucially unranked with the faithfulness constraints that militate against the processes. The result of this is that the output of level 2 will have assibilation half of the time, and that the same situation will hold for liaison.

5.4.2.2 Duke-of-York derivations?

It is worth noting at this point that another approach that could have been pursued in order to obtain optional liaison and/or assibilation would have been for the insertion of linguistic material to occur and then be reversed by deletion. For instance, for optional liaison a consonant could be inserted on level 2 every time, but be deleted on level 3 half the time. This may seem an unlikely solution at this point, however as we will see, QF does have a general tendency to delete both consonants and vowels, and level 3 would certainly lend itself well to variable deletion. Moreover, this type of Duke-of-York derivation (one in which a form changes, and then changes back: X Y X) is a clear possibility in a multi-level system, unless a mechanism is posited to stop it. As we will see, there is no reason to rule out Duke-of-York derivations, and so this is not a reason for rejecting this approach.

However, particularly in the case of the liaison consonants, it seems problematic to claim that they appear and subsequently delete. Although the reasons for consonant deletion will not be addressed in depth in 5.4.3, the process serves to satisfy well-formedness constraints. The likely main motivation for consonant deletion in QF is avoiding bad clusters, and the fact that certain consonants such as / l/ seem to violate well-formedness even on their own also comes into play.

But for examples such as 129, an insertion and deletion analysis would require that / t/ appears between [ e] and [ œ] and then deletes.

129

129. C’ est un ami? [seœ] (=/ s#e#œ/) ‘He’s a friend? (I/46/13)

But it would be very difficult to explain why [ t] deletes from this context. Firstly, this deletion creates hiatus from a perfectly good VCV sequence. Secondly, [ t] makes for an excellent onset for [ œ] and its deletion compromises the perceptual salience of [ œ]. Finally, if a high-ranking constraint forced deletion of a segment for reasons of economy, it would much better to get rid of [e]. These considerations support the presence of optionality on level 2.

5.4.2.3 Categorical processes in level 2

While liaison and assibilation must be optional on this level, the behaviour of h-aspiré will stay the same as in level 1. Although none of the data here can definitely confirm that h-aspiré items behave the same across clitic groups as within them, there does not seem to be any reason to believe that this is not true. For this reason, on level 2 h-aspiré words will keep the abstract segment and the constraint targeting it will remain highly ranked.

Finally, while hiatus is unlikely within the clitic group, it is quite common within the phonological phrase. Importantly, as we have seen in Chapter 3, glide insertion is a possible way to resolve hiatus, but it occurs quite infrequently across words. Therefore, there has to be an explanation for why the anti-hiatus constraint is not consistently satisfied by glide insertion within the phonological phrase. A solution to this is to claim that glide insertion is not actually possible on level 2 at all. Because this level has been set up to allow for optionality but not for more complex patterns of variability, in particular rare variants, claiming that glide insertion is impossible fits well. Since glide insertion is very similar to liaison in that it involves epenthesis of a non-vowel, glide insertion can be barred through the mechanism that would license liaison consonants. As discussed in Chapter 2, the approach advocated for here makes a significant idealization: that a set of constraints could be formulated in a way that would correctly predict which liaison consonant surfaces in which context. If for each possible liaison consonant, there existed a constraint that licensed its presence in specific contexts (defined phonologically and/or morphologically), this same set of constraints could serve to disallow the insertion of a glide,

130 seeing as glides would never be licensed. 49 Further evidence that glide insertion between clitic groups ought to take place on level 3 comes from the fact that it is possible before an h-aspiré item. On level 2, the abstract segment is still present, meaning that the motivation for glide insertion is not yet there. Glide insertion is only one of many forms of hiatus resolution that occur variably: diphthong and glide formation will also only take place on level 3, meaning that a constraint that forces vowels to make up nuclei will be highly ranked as well.

5.4.2.4 Constraints and comparison with level 1

Figure 30 provides the constraint formulations for the second constraint level. Figure 31 shows the ranking of these constraints, with crucially unranked constraints shown separately but on the same (bottom) tier, while constraints for which insufficient evidence exists to fine-tune the ranking are shown in a group on one tier.

Figure 30. Constraint formulations for Level 2: Phonological Phrase

49 Adopting this approach explains why glide insertion within the word (as in / lu+e/ → [ luwe] louer ‘to rent’) is far more frequent in QF than across the word. The insertion of a glide sharing the features of a preceding vowel could take place within a clitic group on level 1, but not within a phonological phrase on level 2.

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Figure 31. Constraint ranking for Level 2: Phonological Phrase

MAX | *h↔C *[strident][back] V=Nucleus

*VV [strident]/_[high] DEP

The constraint sets and rankings for levels 1 and 2 are quite similar. The basic difference is that

DEP has been promoted to the same level as the two constraints that were on the second-to- bottom tier in level 1. However, if other processes of QF phonology were taken into account, the differences between levels 1 and 2 would likely increase (for instance high vowel laxing should almost certainly be limited to level 1: see Walker 1984: 3.2). Despite the apparent similarities between levels, the conceptual difference between them is very significant. Processes confined to level 1 are categorical while those confined to level 2 are optional. This is at the heart of the model: as we go up the strata, the potential resulting distributions are increasingly complex.

5.4.3 Level 3: Intonational Phrase

The final constraint level applies to a large prosodic domain, namely the intonational phrase. All of the cases of liaison and assibilation that need to be accounted for have taken place on levels 1 and 2, so no constraints relating to these processes need to be explicitly ranked on level 3. The most significant work done on level 3 has to do with the anti-hiatus repairs done between words that were the focus of Chapter 4. These primarily involve deletion, either of entire segments or of components such as features. This means that MAX , which was undominated in levels 1 and 2, will be significantly demoted. In keeping with this, this third level is also the one in which the abstract / h/ at the beginning of h-aspiré words deletes. To account for this deletion, a constraint that bars this segment from appearing is highly ranked, as is a constraint prohibiting the insertion

132

50 of a feature that could make the illicit / h/ an acceptable consonant. Also, in order to correctly account for the surface forms in the data, additional constraints that simply militate in favour of the deletion of either consonants or vowels have been added. As discussed above, this is meant to account for the fact that QF has a general tendency to delete phonological material. This tendency is instantiated simply as *V and *C, with the recognition that these constraints stand in for much more complex patterns. 51 Following the findings of Chapter 4, the evaluation on level 3 is stochastic. While the ranking of the core constraints is the one obtained by OTSoft (see section 4.3), the task of arriving at a complete ranking for all of the constraints is too large. For this reason, rather than giving exact values for the constraints on this level, they are presented in order of ranking value, either as determined by OTSoft or as estimated based on the data at hand.

5.4.3.1 Constraints and comparison with other levels

The constraints are given in Figure 32 in order from strongest to weakest. Although the constraints are ranked along a continuous scale, some outcomes are so rare that the constraints can be thought of as dominating the other ones in a manner similar to traditional OT (see section 4.1.3.1 for a discussion of the ranking value relationships resulting in categorical distributions). In particular, given that / h/ does not surface, the constraint that prohibits it, which is listed first, can be thought of as having a much higher ranking value than the subsequent constraints.

50 Working this out in full detail would require settling on a contrastive hierarchy for the QF consonant system, which falls outside of the scope of this work. For now, I assume that all of the consonants of QF would have at least one contrastive feature that / h/ does not have.

51 *V does roughly the same work as would a constraint in favour of syllable economy (as in Tranel 1999), especially in conjunction with *C. Although there is a general tendency to delete vowels, the constraint *VV remains necessary despite *V given that there is much more deletion of vowels when they are in hiatus context. *C can best be seen as an umbrella term for a family of constraints targeting consonant cluster well-formedness (see Côté 2000), with the additional caveat that in some infrequent cases a consonant deletes from in between two vowels.

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Figure 32. Constraint formulations for Level 3: Intonational Phrase

*h: The segment / h/ must not appear. DEP : Every feature/mora in the output must correspond to a feature/mora in the input. MAX [F]: Every feature in the input must correspond to a feature in the output. NODIPHTHONG : A nucleus must not be made up of two vowels. *VV: Two vowels must not appear in a row. MAX V1: The first of two vowels in a row in the input must correspond to a vowel in the output. MAX WORD INITIAL : Every word-initial vowel in the input must correspond to a vowel in the output. MAX ROOT NODE : Every segment in the input must correspond to a segment in the output. V=N UCLEUS : Every segment corresponding to a vowel in the input must make up a unique nucleus. *C: Consonants must not appear. *V: Vowels must not appear.

The constraint set and ranking for level 3 is significantly different from levels 1 and 2. As mentioned, this is where deletion occurs, and so some of the MAX constraints are much lower in the ranking than in the earlier levels, which contained a single unified constraint. More importantly however is the fact that unlike the others this level involves stochastic evaluation. This is of course meant to capture behaviour that is less categorical than in earlier parts of the phonology. While level 2 does allow for optionality, level 3 can account for far more complex patterns of variation. Additionally, although the issue of gradience has been ignored here, the model could easily be modified to incorporate gradient outcomes. Segments were considered either present or absent, but there is no doubt that this two-way distinction could have been expanded to differentiate between regular and, for instance, reduced or weakened vowels and consonants. The combination of gradience and variability may give the impression that this level is closer to phonetics than to phonology proper. As discussed in 3.1.2.3, this may be so, and is not at all incompatible with the model being proposed.

5.5 Example Tableaux

The ways this approach operates will be illustrated through a series of tableaux. For every form, three tableaux will be shown, corresponding to each of the three constraint levels. The input to the first level is the underlying form, while in the levels that follow the input consists of the output of the previous level. The output of the last level is the form transcribed in the data. In level 2, crucial non-ranking of constraints is shown with a dotted line. In level 3, a zigzag line is used to indicate that a constraint to the right has a lower ranking value, unless the difference in ranking values is so important that it would lead to categoricity, in which case a solid line is used. Because the way level 3 works is more complicated than the way the first two levels work,

134 the first two series of tableaux (Tableau 17 and Tableau 18) include a more complete illustration of how the winning candidate is selected. The usual symbol is used to identify the attested output, while is used to pick out other possible outputs. Full intonational phrases are given from the beginning, but only the portion of the underlying form that is inside a box is used in evaluating constraint violations. This box is usually given within the phonological transcription, but in the interest of simplicity, in some cases a part of the orthographic transcription is boxed and only this part is transcribed. The tableaux are presented first for glide formation (5.5.1) and diphthong formation (5.5.2), then for liaison (5.5.3), then for coalescence (5.5.4) and deletion (5.5.5), then for h-aspiré (5.5.6), and finally for assibilation (5.5.7).

5.5.1 Glide Formation

Chapter 3 included a discussion of the least dramatic ways of resolving hiatus: turning a high vowel into a glide or having two vowels occupy a single nucleus if there are no high vowels in the input. As we will see, hiatus is also a possible outcome of the contexts leading to diphthong and glide formation. This section discusses gliding, and 5.5.2 diphthong formation. For these first two examples, each level will be discussed in turn while in other sections single tableaux will present levels 1, 2 and 3 at once. In level 1, the underlying form is the input to the constraint set. Each clitic group is evaluated independently on this level, so both words are subject to the constraint set separately. Having two separate boxes in the orthographic representation for the underlying form and a word boundary in the possible outputs shows this. The optimal form for level 1 is the fully faithful candidate, given that none of the markedness constraints penalize the structures in the underlying form. The functioning of the constraints is nonetheless shown where possible. Despite there being no motivation for them, violations of MAX , *VV and DEP are shown, bearing in mind that *VV only applies within the clitic group and not across the word boundary.

135

Tableau 17. Level 1 for glide formation level 1: CLITIC GROUP

P’is avoir une caméra qui va dans l’eau.

‘And having a camera that goes in the water.’ ↔ AX EP /pi#avwϪల/ (I/44/19) h M * *[strident][back] *VV [strident]/_[high] D a. pi#avwϪల b. pi#vwϪల *! c. pia#avwϪల *! *

Level 2 takes the optimal form from level 1 as input and the constraint set is evaluated for phonological phrases. In this case, the two words of interest are part of the same phrase and so are evaluated as a single entity. The fully faithful form is once again selected as optimal, although in this case, different constraints conflict. While the best output violates the anti-hiatus constraint, there is no way to satisfy this constraint that doesn’t violate a higher-ranking constraint. 52 At this point in the analysis, only one possibility exists: hiatus.

Tableau 18. Level 2 for glide formation level 2: PHONOLOGICAL PHRASE

P’is avoir une caméra qui va dans l’eau.

C UCLEUS

‘And having a camera that goes in the water.’ ↔ AX EP pi#avwϪల h M * *[strident][back] V=N *VV [strident]/_[high] D a. piavwϪల * b. pavwϪల *! c. pjavwϪల *!

Level 3, which takes the (fully faithful) output of level 2 as input, gets more interesting. Since this set of tableaux considers only two outcomes, glide formation and hiatus, the deciding constraints are *VV and V=N UCLEUS , as illustrated in Tableau 19.

52 An additional possibility, which resolves hiatus through consonant or glide epenthesis and currently appears as though it would be tied with the selected form, would be ruled out through high-ranking constraints licensing liaison consonants (see 5.4.2).

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Tableau 19. Level 3 for glide formation level 3: INTONATIONAL PHRASE

ODE NITIAL

I N P’is avoir ORD OOT [nasal] [back] [round] V1 W R IPHTHONG UCLEUS

‘And having’ D

AX AX AX AX AX AX AX pi#avwϪల EP O *h D M N *VV M M M M V=N *C *V M a. pjavwϪల * ***** ** b. piavwϪల * **** ***

The zigzag lines indicate that a constraint has a higher ranking value than the one(s) to its right, and picks out the attested output and the possible output(s). In this tableau, *VV would appear to be ranked considerably higher than V=N UCLEUS , given that they are separated by four constraints. But the level 3 tableaux should be interpreted through the intermediary of ranking values and selection points, because otherwise they will be misleading. These ranking values are the ones in Table 19 that were arrived at to model the vowel deletion that occurs in level 3. Constraints have numeric values that are meant to be interpreted in relation to one another: close values mean that constraints are closely ranked. In fact the crucial constraints have quite similar ranking values, with *VV at 99.622 and V=N UCLEUS at 98.588. This means that both hiatus and glide formation, still putting aside other types of hiatus resolution, are relatively likely outcomes, explaining why hiatus is flagged as a possible form. As Figure 33 illustrates, the output with glide formation is most likely, given that it is the outcome if both constraints draw selection points from the mean of their distribution. A wide range of other selection points also lead to a higher value for *VV than V=NUCLEUS , all of which would lead to glide formation.

Figure 33. Most likely ranking of *VV and V=NUCLEUS resulting in attested form for glide formation

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However, given how similar the constraint ranking values are, there are also a number of situations where the selection point for V=N UCLEUS would be higher than that for *VV. Such a situation would lead to the possible form in Tableau 19 and is illustrated in Figure 34.

Figure 34. Less likely ranking of *VV and V=N UCLEUS resulting in possible form for glide formation

Finally, the two candidates also differ with respect to *C and *V. *C and *V did not get assigned a ranking value given that they are meant as a shorthand for a series of constraints (see footnote 51), the implementation of which would be too complex here. For the purposes of this example, their ranking can be thought of as significantly lower than that of V=N UCLEUS , meaning that they would seldom affect candidate selection. They have been shown as though they had a relatively low ranking (showing them as higher than the lowest ranked constraint MAX [round], but lower than all other constraints, was meant to be the most neutral choice). In most subsequent tableaux, they do not seem to matter to candidate choice, suggesting that this low ranking may be appropriate. However, in one critical case (optional liaison: see Tableau 26), it seems that *V ought to be ranked significantly higher than where it is shown.

5.5.2 Diphthong Formation

Tableau 20-Tableau 22 show the situation for diphthongization as a method of hiatus resolution. We will again see that hiatus is also possible in the same situation as diphthong formation. As was the case for level 1 in 5.5.1 above, the two words under consideration belong to two independent clitic groups. For this reason, the constraints on level 1 are evaluated separately for each word, again meaning in particular that *VV is not applicable. As for glide formation, the fully faithful candidate is optimal in level 1, as shown in Tableau 20. In this case, only this form is given since no constraints militate for other forms.

138

Tableau 20. Level 1 for diphthong formation level 1: CLITIC GROUP

Ah non, moi, un gars pas intéressé, ciao, bye, il y en a d’autres. ‘Oh, no, to me, a guy who’s not interested,

ciao, bye, there are others.’ ↔ AX EP /mwa#œ/ (I/8/6) h M * *[strident][back] *VV [strident]/_[high] D a. mwa#œ

The optimal candidate from level 1 becomes the input to level 2. Here both words belong to the same phonological phrase, and so all of the level 2 constraints are evaluated for the sequence as a whole. As for glide formation, the only possible output on level 2 is hiatus. The form with glide formation cannot surface at this point because V=N UCLEUS is in the tier that dominates the *VV tier.

Tableau 21. Level 2 for diphthong formation level 2: PHONOLOGICAL PHRASE

Ah non, moi, un gars pas intéressé, ciao, bye, il y en a d’autres. ‘Oh, no, to me, a guy who’s not interested,

C UCLEUS

ciao, bye, there are others.’ ↔ AX EP mwa#œ h M * *[strident][back] V=N *VV [strident]/_[high] D a. mwaœ * b. mwa⍝œ *!

Level 3 is where diphthong formation can occur. With the output of level 2 as input, both diphthong formation and hiatus are likely outcomes as Tableau 22 illustrates.

Tableau 22. Level 3 for diphthong formation level 3: INTONATIONAL PHRASE

ODE NITIAL

I N moi, un ORD OOT [nasal] [back] [round] V1 W R IPHTHONG UCLEUS

‘to me, a’ D

AX AX AX AX AX AX AX mwaœ EP O *h D M N *VV M M M M V=N *C *V M a. mwa⍝œ * * ** * b. mwaœ * ** **

139

Here, the two most important constraints, NODIPHTHONG and *VV, only differ in ranking value by 0.005 (see Table 19). With a difference that small, the constraints are virtually tied, meaning that the ranking leading to the attested form is as likely to occur as that producing the candidate marked as possible. The functioning of the three levels for diphthong formation is therefore almost identical to that for gliding. When we isolate those forms of hiatus resolution, the constraints work fairly simply to produce variation, which is the result of the stochastic evaluation of level 3. The following sections will show the three levels together, for other forms of hiatus resolution.

5.5.3 Liaison

Between a clitic and a noun or verb, liaison is typically obligatory. This arises in two different ways: in the majority of cases, a liaison consonant is inserted on level 1 as in Tableau 23, while for a small group of clitics (see 2.3.6.1) the liaison consonant is provided in the underlying form as in Tableau 24.

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Tableau 23. Obligatory liaison with epenthesis level 1: CLITIC GROUP

Comme un enfant.

‘Like a child.’ ↔ AX EP /kѐm# œ#afa / (I/40/4) h M * *[strident][back] *VV [strident]/_[high] D a. œnafa * b. œafa *! c. œfa *! level 2: PHONOLOGICAL PHRASE

Comme un enfant.

C UCLEUS

‘Like a child.’ ↔ AX EP kѐm#œnafa h M * *[strident][back] V=N *VV [strident]/_[high] D a. kѐmœnafa b. kѐmœafa *! *

level 3: INTONATIONAL PHRASE

ODE NITIAL

I N Comme un enfant. ORD OOT [nasal] [back] [round] V1 W R IPHTHONG UCLEUS

‘Like a child.’ D

AX AX AX AX AX AX AX kѐmœnafa EP O *h D M N *VV M M M M V=N *C *V M a. kѐmœnafa **** **** b. kѐmœafa * * *** ****

141

Tableau 24. Obligatory liaison with underlying consonant level 1: CLITIC GROUP

Parles-en.

‘Talk about it. ↔ AX EP /paలl#za/ (I/19/11) h M * *[strident][back] *VV [strident]/_[high] D a. paలlza b. paలla *!

level 2: PHONOLOGICAL PHRASE

Parles-en.

C UCLEUS

‘Talk about it. ↔ AX EP paలlza h M * *[strident][back] V=N *VV [strident]/_[high] D a. paలlza b. paలla *!

level 3: INTONATIONAL PHRASE

ODE NITIAL

I N Parles-en. ORD OOT [nasal] [back] [round] V1 W R IPHTHONG UCLEUS

‘Talk about it. D

AX AX AX AX AX AX AX paలlza EP O *h D M N *VV M M M M V=N *C *V M a. paలlza **** ** b. paలla * * *** **

Following these models, hiatus will be resolved through liaison following many clitics, such as determiners and pronouns. Chapter 2 described the absence of hiatus for pronouns that do not take liaison consonants as resulting from allomorphy, in for example the case of second person singular tu . In the same chapter, a first look at the pronoun ils (3 p. pl.) assumed that it was / i/ in its underlying form, but surfaced as the equivalent glide rather than triggering liaison because of a constraint penalizing the high front vowel. This approach will not work here because of the approach to features advocated for in Chapter 3 and Chapter 4. Indeed, the only features /i/ bears are [ATR] and [high] and both are shared by the other high vowels, meaning that a constraint prohibiting / i/ from surfacing will also affect / u, y/, which is an undesirable outcome. More

142 importantly however is the finding that ils always surfaces as a glide pre-vocalically, in contrast to other tokens of / i/ in hiatus context (see 4.2.3). Moreover, this pattern mirrors that of the feminine pronoun, which can best be analyzed through allomorphy. For this reason, the preliminary analysis from Chapter 2 ought to be rejected in favour of an approach where the glide is provided in the underlying representation for ils , as well as the singular il and the existential il y , as illustrated in Tableau 25.

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Tableau 25. No liaison with ils level 1: CLITIC GROUP

Il a dit, “je veux parler à C.”, ils ont rien voulu savoir, il a po’gné les nerfs. ‘He said, “I want to talk to C.”, they didn’t

want to hear it, he freaked out. ↔ AX EP /j#ѐ/ (I/27/10) h M * *[strident][back] *VV [strident]/_[high] D a. jѐ b. iѐ *!

level 2: PHONOLOGICAL PHRASE

Il a dit, “je veux parler à C.”, ils ont rien

voulu savoir, il a po’gné les nerfs. _[high] ‘He said, “I want to talk to C.”, they didn’t

C UCLEUS

want to hear it, he freaked out. ↔ AX EP jѐ h M * *[strident][back] V=N *VV [strident]/ D a. jѐ b. iѐ *!

level 3: INTONATIONAL PHRASE

ODE NITIAL

I N ils ont ORD OOT [nasal] [back] [round] V1 W R IPHTHONG UCLEUS

‘they didn’t’ D

AX AX AX AX AX AX AX jѐ EP O *h D M N *VV M M M M V=N 53*C *V M a. jѐ * * * b. iѐ * **

The final complication due to liaison has to do with optional liaison between the verb être ‘to be’ and its complements. As discussed in 5.4.2, this will be modeled through crucial unranking of the constraints in level 2. This leads to two possible outputs in level 2, as well as a situation where

53 For the sake of simplicity, all glides will be treated as consonants for the purposes of this constraint. This is awkward in certain cases, but seems appropriate because *C is meant to penalize bad clusters. In any case, given the stochastic evaluation, this will not have major negative consequences.

144 level 3 can serve to mask the motivation for consonant insertion, as illustrated in Tableau 26. The two outputs of level 2 are labeled as 1 and 2 in level 3.

Tableau 26. Optional liaison level 1: CLITIC GROUP

C’est un trip d’une vie.

‘It’s the thrill of a lifetime.’ ↔ AX EP p/ (I/36/7) hگs#e#œ#tల/ M * *[strident][back] *VV [strident]/_[high] D pگa. se#œtల

level 2: PHONOLOGICAL PHRASE

C’est un trip d’une vie.

C UCLEUS

‘It’s the thrill of a lifetime.’ ↔ AX EP h [p strident]/_[highگse#œtల M * *[strident][back] V=N *VV [ D * pگa. setœtల * pگb. seœtల

level 3: INTONATIONAL PHRASE

ODE NITIAL

I N C’est un trip d’une vie. ORD OOT [nasal] [back] [round] V1 W R IPHTHONG UCLEUS

‘It’s the thrill of a lifetime.’ D

AX AX AX AX AX AX AX p EP Oگsetœtల .1 *h D M N *VV M M M M V=N *C *V M ** ***** * * * pگa. stœtల *** ***** pگb. setœtల pگseœtల .2 *** **** * pگa. seœtల ** **** * * * pگb. sœtల * ** **** * * * pگc. setల ** **** * * pگd. se⍝œtల

Output 1a (the attested candidate on level 3) is the opaque candidate. This is the one instance that suggests that *V ought to have a higher ranking than what is shown, since it is the only constraint that favours the winning form. Of course, in the context of stochastic evaluation, the ranking as it stands could still produce this form, since it would be possible for *V to have a greater selection

145 point than any of the other relevant constraints for one particular evaluation. However, this opaque form and similar ones are relatively frequent and seem very natural. In order to account for this pocket of the data, it seems that it would be better to show it as a much likelier output than it is now. In fact, raising the ranking of *V to somewhere around that of MAX V1 solves this problem with no negative consequences to the analyses of other forms. Because *V is only critical here, and because there is no concrete evidence for a specific ranking, it will continue to be shown where it is, but should be thought of as having a higher ranking.

5.5.4 Coalescence

Other types of hiatus resolution involve reducing the two vowels in sequence to a single vowel. In the case of coalescence, aspects of both underlying vowels contribute to creating a new surface vowel. Tableau 27 shows how such an outcome is possible following the approach advocated for in Chapter 3.

146

Tableau 27. Coalescence level 1: CLITIC GROUP

P’is elle dit, “à un moment donné”, elle dit, “j’avais une coach de deux cent cinquante livres-” And she says, “once”, she says, “I had a two

hundred and fifty pound coach-” ↔ AX EP /pi#a/ (I/27/24) h M * *[strident][back] *VV [strident]/_[high] D a. pi#a

level 2: PHONOLOGICAL PHRASE

P’is elle dit, “à un moment donné”, elle dit, “j’avais une coach de deux cent cinquante livres-” And she says, “once”, she says, “I had a two

C UCLEUS

hundred and fifty pound coach-” ↔ AX EP pi#a h M * *[strident][back] V=N *VV [strident]/_[high] D a. pia *

level 3: INTONATIONAL PHRASE

ODE NITIAL I

p i a N

+ATR +low ORD OOT [nasal] [back] [round] V1 W R IPHTHONG UCLEUS

+high D

AX AX AX AX AX AX AX EP O *h D M N *VV M M M M V=N *C *V M 54 a. p i + a = p ϯ * * * * +ATR +low +high

b. pia * * ** c. pa * * * * d. pja * ** *

54 It is not clear how to evaluate MAX V1 and MAX WORD INITIAL for tokens of coalescence.

Given that MAX WORD INITIAL is motivated by the claim that the presence of word-initial vowels aids in lexical access, it would seem unfair to claim that a candidate with coalescence did not violate this constraint. On the other hand, it seems wrong to claim that both constraints have been violated given that a vowel is present. For this reason, coalescence candidates are shown with a violation for MAX WORD INITIAL but not for MAX V1.

147

The attested case of coalescence results from feature combination, while a number of other forms are also possible, which is the desired outcome. The present ranking would predict coalescence too frequently, but this problem could easily be solved by positing a relatively high-ranked

IDENT constraint.

5.5.5 Deletion

Deletion is of course more straightforward, involving either the first or second vowel in sequence being erased completely, as in Tableau 28 and Tableau 29 respectively.

Tableau 28. V1 deletion level 1: CLITIC GROUP

Je pense les deux on avait envie de parler, de s’exprimer, p’is ça m’a fait du bien de parler, p’is de- ‘I think both of us felt like talking, like expressing

ourselves, and it did me good to talk, and-’ ↔ AX EP /avϯ#avi/ (I/3/14) h M * *[strident][back] *VV [strident]/_[high] D a. avϯ#avi

level 2: PHONOLOGICAL PHRASE

Je pense les deux on avait envie de parler, de s’exprimer, p’is ça m’a fait du bien de parler, p’is de- ‘I think both of us felt like talking, like expressing

C UCLEUS

ourselves, and it did me good to talk, and-’ ↔ AX EP /avϯ#avi/ h M * *[strident][back] V=N *VV [strident]/_[high] D a. avϯavi *

level 3: INTONATIONAL PHRASE

ODE NITIAL

I N avait envie ORD OOT [nasal] [back] [round] V1 W R IPHTHONG UCLEUS

‘felt like’ D

AX AX AX AX AX AX AX avϯavi EP O *h D M N *VV M M M M V=N *C *V M a. avavi * * ** *** b. avϯavi * ** **** c. avϯvi * * * ** ***

148

Tableau 29. V2 deletion level 1: CLITIC GROUP

Moi je trouve ça tellement beau une femme enceinte. ‘Me, I find that so beautiful, a pregnant

woman.’ ↔ AX EP /bo#yn/ (I/28/19) h M * *[strident][back] *VV [strident]/_[high] D 55 a. bo#௙n

level 2: PHONOLOGICAL PHRASE

Moi je trouve ça tellement beau une femme enceinte. ‘Me, I find that so beautiful, a pregnant

C UCLEUS

woman.’ ↔ AX EP bo#௙n h M * *[strident][back] V=N *VV [strident]/_[high] D a. bo௙n *

level 3: INTONATIONAL PHRASE

ODE NITIAL

I N beau une ORD OOT [nasal] [back] [round] V1 W R IPHTHONG UCLEUS

‘beautiful, a’ D

AX AX AX AX AX AX AX bo௙n EP O *h D M N *VV M M M M V=N *C *V M a. bon * * ** * * b. bo௙n * ** ** c. b௙n * * * ** * *

In the first case of V1 deletion, the attested form would be much preferred to the candidate with

V2 deletion, given that the latter violates the high-ranked MAX [nasal]. In the case of the second vowel deletion tableau, the attested outcome of V2 deletion is only slightly likelier than the possible form with V1 deletion.

55 High vowel laxing would apply within the clitic group.

149

5.5.6 H-aspiré

The second part of Chapter 3 deals with the class of h-aspiré words, which are typically thought of as surfacing with hiatus, as illustrated in Tableau 30.

Tableau 30. Hiatus with h-aspiré level 1: CLITIC GROUP

Je me rappelle, on était en haut, p’is elle a-

‘I remember, we were upstairs, and she was-’ ↔ AX EP /a#ho/ (I/24/9) h M * *[strident][back] *VV [strident]/_[high] D a. aho b. anho *! *

level 2: PHONOLOGICAL PHRASE

Je me rappelle, on était en haut, p’is elle a-

C UCLEUS

‘I remember, we were upstairs, and she was-’ ↔ AX EP a#ho h M * *[strident][back] V=N *VV [strident]/_[high] D a. aho b. anho *! *

level 3: INTONATIONAL PHRASE

ODE NITIAL

I N en haut ORD OOT [nasal] [back] [round] V1 W R IPHTHONG UCLEUS

‘upstairs’ D

AX AX AX AX AX AX AX aho EP O *h D M N *VV M M M M V=N *C *V M a. ao * * ** b. aho *! * ** c. ano * * *

But as we saw, other outcomes are also possible with h-aspiré words, as Tableau 31 illustrates for vowel deletion and Tableau 32 for glide insertion.

150

Tableau 31. Vowel deletion with h-aspiré level 1: CLITIC GROUP

C’est aussi cruel de mettre un homard vivant dans de l’eau chaude. ‘It’s as cruel putting a live lobster in hot

water.’ ↔ AX EP /œ#homaల/ (I/42/21) h M * *[strident][back] *VV [strident]/_[high] D a. œ#homaల level 2: PHONOLOGICAL PHRASE

C’est aussi cruel de mettre un homard vivant dans de l’eau chaude. ‘It’s as cruel putting a live lobster in hot ent]/_[high]

C UCLEUS

water.’ ↔ AX EP œhomaల h M * *[strident][back] V=N *VV [strid D a. œhomaల level 3: INTONATIONAL PHRASE

ODE NITIAL

I N un homard ORD OOT [nasal] [back] [round] V1 W R IPHTHONG UCLEUS

‘a lobster’ D

AX AX AX AX AX AX AX œhomaల EP O *h D M N *VV M M M M V=N *C *V M a. œmaల * * * ** ** b. œomaల * ** *** c. œhomaల * *** *** d. omaల * * * ** **

151

Tableau 32. Glide insertion with h-aspiré level 1: CLITIC GROUP

Moi, je le donnerais au hasard parce que je saurais pas à qui le donner. ‘Me, I would give it at random because I

wouldn’t know who to give it to.’ ↔ AX EP /o#hazaల/ (I/15/13) h M * *[strident][back] *VV [strident]/_[high] D a. ohazaల

level 2: PHONOLOGICAL PHRASE

Moi, je le donnerais au hasard parce que

je saurais pas à qui le donner. [back] ‘Me, I would give it at random because I

C UCLEUS

wouldn’t know who to give it to.’ ↔ AX EP ohazaల h M * *[strident] V=N *VV [strident]/_[high] D a. ohazaల

level 3: INTONATIONAL PHRASE

ODE NITIAL

I N au hasard ORD OOT [nasal] [back] [round] V1 W R IPHTHONG UCLEUS

‘at random’ D

AX AX AX AX AX AX AX ohazaల EP O *h D M N *VV M M M M V=N *C *V M a. owazaల * *** *** b. oazaల * ** *** c. ozaల * * ** **

In Tableau 31, the attested form with vowel deletion is shown to be quite likely despite the expectation that h-aspiré items have hiatus, while in Tableau 32, the attested form with glide insertion is rather unlikely. Indeed, in the first case the attested form is actually the likeliest although by a small margin, while in the second case the attested form is quite unlikely as it violates the second highest ranking constraint. This is as it should be: deletion is much, much more frequent than glide insertion overall, and nothing in the h-aspiré data or how it was analyzed suggests that these items should be any different.

5.5.7 Assibilation

Finally, the interaction of assibilation and deletion is illustrated in Tableau 33.

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Tableau 33. Assibilation and coalescence level 1: CLITIC GROUP

On est - tu obligées ?

‘Do we have to?’ ↔ AX EP /ѐ#e#ty#obliЋe/ (I/31/6,7) h M * *[strident][back] *VV [strident]/_[high] D a. ѐne#tsy#obliЋe ** b. ѐe#tsy#obliЋe *! * c. ѐne#ty#obliЋe *! *

level 2: PHONOLOGICAL PHRASE

On est - tu obligées ?

C UCLEUS

‘Do we have to?’ ↔ AX

s EP ѐne#t y#obliЋe h M * *[strident][back] V=N *VV [strident]/_[high] D a. ѐnetsyobliЋe *

level 3: INTONATIONAL PHRASE On est-tu obligées?

‘Do we have to?’

s ѐnet y o bliЋe

ODE NITIAL I

+ATR +ATR ORD OOT [nasal] [back] [round] V1 W R IPHTHONG UCLEUS

+high +back D

AX AX AX AX AX AX AX + round +round EP O *h D M N *VV M M M M V=N *C *V M a. y + o = u * * * +ATR +ATR +ATR +high +back +back + round +round +high + round

b. yo * ** c. o * * * * d. y * * * * * d. іo * * *

Both the attested output in 3a and the possible output in 3c are opaque. This final tableau shows how the model handles such cases naturally. A number of non-opaque outputs are also possible, but the opaque cases result in a straightforward way from the organization of the levels.

153

With the important caveat that *V must have a higher ranking than initially assumed, these tableaux illustrate how the proposal handles a wide variety of data from QF. Multiple possible outputs can be predicted, and an explanation for the existence of rare variants is provided by the different ranking values of constraints. The interaction of processes can be modeled by having some apply at earlier levels than others. This also allows for a more abstract analysis of some phenomena, in this case h-aspiré, than single-level evaluation models might afford.

5.6 Summary

In this chapter, the aspects of hiatus and hiatus resolution discussed in the previous chapters were brought together into a proposal for modeling the relevant processes. Because a number of these processes involve opacity, I argued that a multi-level grammar works best. The basic architecture is one with three levels made up of constraints, with the levels being serially linked. The levels are mapped to different prosodic constituents, with the first corresponding to the clitic group, the second to the phonological phrase, and the third to the intonational phrase.

The first level has the properties of standard OT, with a single optimal candidate being selected as output for every given input. This makes it possible to capture the categorical nature of some processes, such as word-internal assibilation and liaison between a clitic and host. The second level allows some constraints to be crucially unranked with respect to one another, to account for optional processes. This means that for some processes with two outcomes that are roughly equally likely, level 2 will yield two possible outputs. Processes that warrant this mechanism include assibilation across word boundaries and liaison between some forms of the auxiliary être ‘to be’ and a participle. Finally, the third level has stochastic evaluation to model the variable frequencies of a number of processes including glide formation and vowel deletion. Each constraint is assigned a numeric value, but every time the grammar is evaluated, a measure of random noise is introduced. This results in the ability to predict a range of possible outputs for any given input.

The cases of opacity are explained by having the interacting processes take place in different strata. The cases of assibilation where a stop has become an affricate despite the absence of a high front vowel trigger are due to assibilation taking place on level 1 (since they all involve word-internal assibilation) followed by vowel deletion on level 3. The cases of liaison where an epenthetic consonant is present despite not being between two vowels are due to liaison taking

154 place on level 1 or 2 (depending on the size of the prosodic constituent) followed by vowel deletion on level 3. Finally, the behaviour of h-aspiré items is attributed to having constraints that target an abstract segment on levels 1 and 2, and a constraint that forces the deletion of this abstract segment on level 3.

Even though many processes are unified into a single model, this does not mean that different types of data and phenomena are explained in the same way. The character of different processes can vary greatly depending on where in the grammar they are situated, and what kinds of mechanisms are used to account for them. For instance, liaison can result either from consonant epenthesis or from an underlying consonant, and can be either optional or obligatory. Also, because the different strata are made up of different constraint sets, some tendencies can be given limited scope (by having a markedness constraint that only appears in one level, for instance) while other tendencies can have a strong effect throughout the system (by having high-ranked markedness constraints on more than one level, for instance). In this way, each phonological phenomenon has a unique analysis even though everything is integrated into one model.

155

Chapter 6 Conclusion

This thesis contains a number of arguments about hiatus, about hiatus resolution, about Québécois French (QF), and about phonology. The first of these is methodological, and will be reviewed in 6.1. The arguments about the phonology of QF, some with implications for other languages and others more limited in scope, are summarized in 6.2. In 6.3, I present an overview of the proposed model. Finally, in 6.4 I discuss one of the most important remaining issues, learnability.

6.1 Methodology

The data that form the basis of the thesis are quite messy: they are unevenly distributed, and in some instances look as though they involve phonetic phenomena, rather than phonological processes. The choice of relying heavily on data from a single speaker may also be unorthodox. However, the trade-off is worth it in order to end up studying speech that is naturalistic and truly reflects the way speakers of QF use the language, and to ensure that variability in the data is the result of a single grammar. Choosing these data meant that it was very useful to employ a range of quantitative and statistical methods of analysis. I adopted a general approach in keeping with variationist methodology and the key concept of variable context. But rather than using these tools to describe trends at the surface, I use them to explore deeper and more abstract levels of phonology. Even though phones are at the heart of this research, they are used to explore phonemic structure, the organization of phonemes, and the architecture of the phonological module. I argued that where processes that may appear low level involve manipulating abstract entities, they belong in phonological models. I hope to have provided an example of concrete data being successfully used to engage productively with theoretical debates, and in particular, an example of the compatibility of this type of research with formal phonology.

Also, the approach to coalescence and features was innovative in its methodology. I used the coalescence data to determine which of thousands of possible feature sets was most compatible, taking other aspects of QF phonology into consideration. This was made possible by CoalMiner (Scott & St-Amand 2009), a tool that I can make available to other researchers. With the help of this software, I was also able to compare binary and privative features in terms of how well they

156 fit with the data, and on this basis argued that the latter were superior. In addition, I was able to compare feature sets organized in a contrastive hierarchy (Dresher 2009) with feature sets that have full specification, and found reason to prefer the former to the latter.

6.2 Phonology of QF

With respect to conclusions about phonology, I present arguments that apply very specifically to QF, and others that have wider implications. I argue that there is evidence for a QF tendency to avoid hiatus, and that it is multiply instantiated in the grammar, but in ways that can be masked. In a constraint-based model, having a constraint *VV (No hiatus) that is dominated by other constraints can capture the fact that this tendency is indeed operative in QF, but that forms that go against it do surface. Additionally, I provide support for the view that the majority of schwas and liaison consonants are epenthetic, and in the case of liaison, that some exceptional cases are best analyzed as underlying (following Côté: see 2008 and references therein). I also argued for the pronominal system of QF involving significant allomorphy, and for h-aspiré items beginning with an abstract segment.

Moving towards issues with a broader impact, I argue that assibilation, liaison and h-aspiré exhibit opacity, and that this opacity is best modeled in a multi-level grammar (following Kiparsky 2000, 2010 and Rubach 2000 among others). The serialism of multi-stratal grammars handles opacity in a simple and natural way that has yet to be rivaled by single-level proposals. I argue that QF defines its three constraint levels according to larger prosodic constituents than have been proposed for English, with three levels corresponding to the clitic group, the phonological phrase and the intonational phrase. This makes it a consequence of the proposal that different languages can define different prosodic domains as targets for their constraint levels.

I also argue that the rampant variability exhibited by QF needs to be accounted for in the phonology proper. I do so by incorporating stochastic evaluation into the proposed grammatical architecture. In a model with three serially linked levels, this variation results from the probabilistic nature of the third level. Importantly, the first level only produces categorical outputs while the second produces optionality, but no more complex variability. This provides the ability to limit the possible outputs of the model in ways that fit well with observed patterns. The fact that the three levels can have different constraint rankings means that a given process

157 can be made obligatory across the board in level 1 and never reversed by the effect of subsequent constraints, or conversely that a form can be ruled out in level 1 and never made possible in level 2 or 3. Despite the range of possibilities that the stochastic level predicts, it is certainly not the case that “anything goes”. The ability to predict both categorical and massively variable behaviour is definitely a strong point of the model.

It is the incorporation of stochastic evaluation and multi-level architecture that makes this proposal different from others. Whether this model can be extended to other varieties of French and to other languages depends on whether they exhibit similar variability and opacity. This is an empirical question, but there is good reason to believe that other phonologies also warrant these mechanisms. On the one hand, the extensive international literature in variationist sociolinguistics suggests that variability is indeed pervasive cross-linguistically (see for instance the research reported in Chambers et al. 2002 which covers a huge range of languages and varieties), and on the other hand the extensive literature on interacting processes also spans the languages of the world (see Odden 2005: Chapter 5; Gussenhoven & Jacobs: Chapter 6 and references therein).

6.3 Proposed model

The final proposal is made up of three levels of constraints. The first level has the architecture of standard Optimality Theory (OT: Prince & Smolensky 1993/2004), resulting in categorical outputs. The second level takes standard OT and adds crucially unranked constraints (Anttila 1997), which allows for optionality. The third level is a bigger departure from the two previous ones: it uses Stochastic OT (Boersma & Hayes 2001) to allow for variation that is more complex than the optionality of level 2. These levels are linked in a serial manner. An underlying form /A/ is submitted as input to level 1. The optimal candidate from level 1, an intermediate form B, becomes the input to level 2. Level 2 outputs a second intermediate form C, which is submitted to level 3. The selected candidate from level 3 is the surface form [D]. This situation is schematized in Figure 35: see Figure 27, Figure 30, and Figure 32 in Chapter 5 for the constraint formulations for the three levels. 56

56 And also for a discussion of the level 3 constraints shown as ‘nr’ (not ranked by OTSOft).

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Figure 35. Inputs and outputs in proposed model

/A/

MAX |

*h↔C *[strident][back] | Level 1 *VV [strident]/_[high] | DEP

MAX | *h↔C *[strident][back] V=Nucleus Level 2

*VV [strident]/_[high] DEP

Constraint Ranking Value *h nr DEP 103.198 MAX [nasal] 102.367 NODIPHTHONG 99.627 *VV 99.622 MAX [back] 99.029 MAX V1 98.780 Level 3 MAX WORD INITIAL 98.771 MAX ROOT NODE 98.592 V=N UCLEUS 98.588 *C nr *V nr MAX [round] 33.733

[D]

159

Figure 35 shows an underlying form being submitted to the constraints in level 1. This level operates over clitic groups, and its outputs are categorical. The next set of constraints, in level 2, applies to phonological phrases, and allows for optionality. Finally, the third set of constraints targets the intonational phrase and produces variable outputs, which are the final outputs of the model. The serial relationship between levels allows the model to account for interacting processes: the effect of constraint satisfaction in an earlier level can be undone or hidden by constraint satisfaction at a later level. In this way different types of data, undergoing a range of phonological processes, manage to be integrated into a unified model.

6.4 Learnability

One of the most crucial issues for future research has to do with the learnability of the model. The proposed model is relatively complex (involving multiple constraint sets and multiple methods of constraint evaluation) and relatively abstract (involving features as well as segments that never surface). But this need not count as a strike against its learnability, given that there seem to be multiple points of potential bootstrapping. The claim that the features of a language’s inventory are organized hierarchically limits the possible feature specifications considerably. It also seems plausible that learning the feature hierarchy of a language could be guided by general principles that apply across languages, which would aid considerably in the acquisition of the constraint set for level 3. Also, the fact that constraint levels correspond to specific prosodic domains ought to contribute to the ease of learning. Even though I argue that the mapping is language-specific, the limited number of prosodic constituents means that there are a small number of possible mappings. This again means that positing some principle through which learners instinctively seek to match a prosodic constituent with a constraint level would contribute to the learnability of the model. The absence of deletion from the first two constraint levels was also a feature of the model here. If this, or a similar property, could be shown to apply across languages, this could also significantly reduce the work of acquisition. Finally, if it could be shown that the stratification of categorical, optional, and variable outputs was not restricted to QF, this would constitute a tremendous source of evidence for the learner. It is certainly possible that variation, rather than being a messy hurdle for a learner to overcome, could actually be key in acquiring phonology.

160

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Appendices

Appendix A : Average F1 (on y-axis) and F2 (on x-axis) for the main speaker

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Appendix B : Ultimately unsuccessful feature hierarchy

The set of feature specifications that accounts for the most cases of coalescence is in Table 21 below.

Table 21. Privative features superior for coalescence but ultimately unsuccessful i y e ø ϯ ϯ œ œ a a Ϫ ѐ ѐ o u [+ATR] [–back] [+high] [+low] [+nasal] [–round] Figure 36 gives a contrastive feature tree for one of a number of possible feature hierarchies that result in the same set of feature specifications, in two parts, separated following the first split according to [+ATR].

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Figure 36. Plausible feature hierarchy for privative features superior for coalescence but ultimately unsuccessful

feature hierarchy: [ATR] > [high] > [low] > [back] > [round] > [nasal] active values: [+ATR], [–back], [+high], [+low], [+nasal], [–round]

[+ATR]

[+high] (–high)

[–back] (+back) [–back] (+back) u o

[–round] (+round) [–round] (+round) i y e ø

(–ATR)

[+low] (–low)

[–back] (+back) [–back] (+back) Ϫ

[–round] (+round)

[+nas] (–nas) [+nas] (–nas) [+nas] (–nas) [+nas] (–nas) a a œ œ ϯ ϯ ѐ ѐ

The list of the possible feature hierarchies that result in the same set of feature specifications as the hierarchy in Figure 36 is in Table 22 below.

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Table 22. Possible feature orderings for Table 21

The possible feature orderings that result in the contrastive feature specifications given in Table 21. The privative values are [+atr], [–back], [+high], [+low], [+nasal], [–round].

atr high low back round nasal low back round atr nasal high atr high low back nasal round low back round nasal atr high atr high back low round nasal low back nasal atr high round atr high back low nasal round low back nasal atr round high atr high back nasal low round low back nasal round atr high atr high nasal back low round back atr high low round nasal atr low high back round nasal back atr high low nasal round atr low high back nasal round back atr high nasal low round atr low back high round nasal back atr low high round nasal atr low back high nasal round back atr low high nasal round atr low back round high nasal back atr low round high nasal atr low back round nasal high back atr low round nasal high atr low back nasal high round back atr low nasal high round atr low back nasal round high back atr low nasal round high atr back high low round nasal back atr nasal high low round atr back high low nasal round back atr nasal low high round atr back high nasal low round back atr nasal low round high atr back low high round nasal back low atr high round nasal atr back low high nasal round back low atr high nasal round atr back low round high nasal back low atr round high nasal atr back low round nasal high back low atr round nasal high atr back low nasal high round back low atr nasal high round atr back low nasal round high back low atr nasal round high atr back nasal high low round back low round atr high nasal atr back nasal low high round back low round atr nasal high atr back nasal low round high back low round nasal atr high atr nasal high back low round back low nasal atr high round atr nasal back high low round back low nasal atr round high atr nasal back low high round back low nasal round atr high atr nasal back low round high back nasal atr high low round low atr high back round nasal back nasal atr low high round low atr high back nasal round back nasal atr low round high low atr back high round nasal back nasal low atr high round low atr back high nasal round back nasal low atr round high low atr back round high nasal back nasal low round atr high low atr back round nasal high nasal atr high back low round low atr back nasal high round nasal atr back high low round low atr back nasal round high nasal atr back low high round low back atr high round nasal nasal atr back low round high low back atr high nasal round nasal back atr high low round low back atr round high nasal nasal back atr low high round low back atr round nasal high nasal back atr low round high low back atr nasal high round nasal back low atr high round low back atr nasal round high nasal back low atr round high low back round atr high nasal nasal back low round atr high

170

Figure 37 shows the details of the coalescences according to this feature hierarchy. For cases where the feature set does not seem adequate, the problematic features are circled.

Figure 37. Coalescences with privative features superior for coalescence but ultimately unsuccessful

Robust coalescences: all successfully accounted for 1. a. a + e = ϯ b. a + e = ϯ +low +ATR –back +low +ATR –back –back –back –round –back –back –round –round +nasal –round +nasal c. i + a = ϯ +high +low –back +ATR –back –round –back –round

Other coalescences: successfully accounted for 2. a. ѐ + a = a b. y + a = œ +nasal +low +low +high +low –back –back –back +ATR –back +nasal –back c. Ϫ + y = œ d. e + œ = ϯ +low +high –back –back –back –back +ATR –round +nasal –round –back +ATR +nasal

Other coalescences: not successfully accounted for 3. a. y + o = u b. e + o = u +high +ATR +high +ATR +ATR +ATR +ATR +ATR –back +high –back –round

c. Ϫ + y = ø d. a + o = ѐ +low +high –back +low +ATR +ATR +ATR –back –back

The robust coalescences in 1 are all nicely accounted for in a way that makes intuitive sense. Similarly, those in 2 are successfully analyzed, with the minor issue related to the [+ATR]

171 feature that is in the input of 2d but not in the result. This is perhaps not a problem since it may be possible to include the feature in the representation of the resulting nasal without any negative effect, given that QF does not have tense nasal vowels. The phonetic implementation for the tense nasal vowel could be identical to that for the lax. Importantly, I am not assuming that structure preservation applies to the feature-based representations. That is, it is possible to have a set of features that is a result of a phonological process but does not exist in the underlying inventory. This seems to be a necessity for QF, in order to account for high vowel laxing. There are not underlying lax high vowels, but it seems that they ought to be represented with features identical to their tense counterpart but without the [+ATR] feature.

The cases in 3 cannot be accounted for with the system here. 3c fails because by stipulation the [+low] feature on V1 cancels both [+high] and [+ATR] on V2. Because 3c has the same input vowels as 2c, but a different output, it seems unlikely that a system could allow for both. A more plausible account that is compatible with the feature set here might be that coalescence has taken place, followed by tensing. 3a, 3b and 3d have more serious problems, but interestingly they all seem related to the feature [back], as discussed in Appendix C.

172

Appendix C : Bivalent [back]

In the three main problematic cases from the most successful hierarchy (Appendix B: 3a, 3b and 3d in Figure 37 above), V1 is [–back], V2 is [ o], and V3 is not [–back] which is what causes the account to fail. It seems that the resulting vowels are getting their front ~ back value from [ o], but this is not captured given that [–back] is the active value. But since SPE (Chomsky & Halle 1968), the fact that [back] is uniquely complex has been recognized (see section 5.2.2 of Dresher 2009). A better analysis of the coalescence cases can be arrived at if we assume that for some segments [–back] is active, but for others [+back] is. While this may seem at first glance like an undesirable hybrid of privative and binary features, viewing this solution as related to context- sensitive markedness (Nevins 2010) may make it more appealing. The claim here would be that a different privative value is active in different parts of the vocalic inventory of QF because in some parts of the inventory [+back] is marked but in others [–back] is. There is not enough coalescence data here to make any sort of a strong claim that QF definitely exhibits context- sensitive markedness in this way, but since other inventories have been argued to show a similar pattern, it is worth exploring.

CoalMiner was modified to allow [back] to have different values for different subsections of the inventory. The most successful set of ranking and values that resulted was nearly identical to the one above, with the single difference that the split within the non-high, [+ATR] vowels operates with the feature [+back] rather than [–back]. Figure 38 shows this change, with the new features in bold.

173

Figure 38. Plausible feature hierarchy for privative features superior for coalescence but ultimately unsuccessful, with bivalent [back]

feature hierarchy: [ATR] > [high] > [low] > [back] > [round] > [nasal] active values: [+ATR], [±back], [+high], [+low], [+nasal], [–round]

[+ATR]

[+high] (–high)

[–back] (+back) (–back) [+back] u o

[–round] (+round) [–round] (+round) i y e ø

(–ATR)

[+low] (–low)

[–back] (+back) [–back] (+back) Ϫ

[–round] (+round)

[+nas] (–nas) [+nas] (–nas) [+nas] (–nas) [+nas] (–nas) a a œ œ ϯ ϯ ѐ ѐ

Having [ o] be specified as [+back] allows for two additional cases of coalescence to be captured, assuming that [–back] and [+back] cancel each other out. Figure 39 shows the new treatment of 3a and 3d, repeated as 4a and 4b.

Figure 39. Additional successful coalescences with bivalent [back]

4. a. y + o = u b. a + o = ѐ +high +ATR +high +low +ATR +ATR +back +ATR –back +back –back

174

Appendix D : Benefits of Contrastive Hierarchy

Putting aside the issue of bivalent [back], the way contrast has been instantiated is a large part of the reason that the set of feature specifications determined to be best by CoalMiner do well. As we have seen, the robust coalescences require privative features, but if every vowel simply bore any privative feature that applied to it, we would fare far worse in accounting for the other coalescences. Crucially, in the best CoalMiner vowel set, the low vowels are not [–round], since the split according to [low] takes place before the [round] split, and [round] is not contrastive within the [+low] vowels. If we were to accept that privative features were required but abandon the need for contrast, or else define contrastive features in a broader way, the low vowels would have to bear [–round]. This would have negative consequences for what were described as other coalescences successfully accounted for in Figure 37 above. Figure 40 below repeats these coalescences, and shows the presence of [–round] to be a major problem for the coalescences in b. and c.

Figure 40. Worse outcome for other coalescences if no Contrastive Hierarchy

2. a. ѐ + a = a b. y + a = œ +nasal +low +low +high +low –back –back –back +ATR –back –round +nasal –back –round –round

c. Ϫ + y = œ d. e + œ = ϯ +low +high –back –back –back –back –round +ATR –round +nasal –round –back +ATR +nasal

Getting rid of the contrastive hierarchy does not improve any of the coalescences that are not successfully accounted for by the best set of feature specifications either. Given this fact, the results of CoalMiner can also be seen to support the view that contrast is determined hierarchically.

175

Appendix E : Possible feature orderings for Table 12

The possible feature orderings that result in the contrastive feature specifications given in Table 12. The privative values are [atr], [back], [high], [low], [nasal], [round]. atr high low round back nasal low atr nasal round back high atr high low round nasal back low round atr high back nasal atr high low nasal round back low round atr high nasal back atr high round low back nasal low round atr back high nasal atr high round low nasal back low round atr back nasal high atr high round nasal low back low round atr nasal high back atr high nasal low round back low round atr nasal back high atr high nasal round low back low round back atr high nasal atr low high round back nasal low round back atr nasal high atr low high round nasal back low round back nasal atr high atr low high nasal round back low round nasal atr high back atr low round high back nasal low round nasal atr back high atr low round high nasal back low round nasal back atr high atr low round back high nasal low nasal atr high round back atr low round back nasal high low nasal atr round high back atr low round nasal high back low nasal atr round back high atr low round nasal back high low nasal round atr high back atr low nasal high round back low nasal round atr back high atr low nasal round high back low nasal round back atr high atr low nasal round back high round atr high low back nasal atr round high low back nasal round atr high low nasal back atr round high low nasal back round atr high nasal low back atr round high nasal low back round atr low high back nasal atr round low high back nasal round atr low high nasal back atr round low high nasal back round atr low back high nasal atr round low back high nasal round atr low back nasal high atr round low back nasal high round atr low nasal high back atr round low nasal high back round atr low nasal back high atr round low nasal back high round atr nasal high low back atr round nasal high low back round atr nasal low high back atr round nasal low high back round atr nasal low back high atr round nasal low back high round low atr high back nasal atr nasal high low round back round low atr high nasal back atr nasal high round low back round low atr back high nasal atr nasal low high round back round low atr back nasal high atr nasal low round high back round low atr nasal high back atr nasal low round back high round low atr nasal back high atr nasal round high low back round low back atr high nasal atr nasal round low high back round low back atr nasal high atr nasal round low back high round low back nasal atr high low atr high round back nasal round low nasal atr high back low atr high round nasal back round low nasal atr back high low atr high nasal round back round low nasal back atr high low atr round high back nasal round nasal atr high low back low atr round high nasal back round nasal atr low high back low atr round back high nasal round nasal atr low back high low atr round back nasal high round nasal low atr high back low atr round nasal high back round nasal low atr back high low atr round nasal back high round nasal low back atr high low atr nasal high round back nasal atr high low round back low atr nasal round high back nasal atr high round low back

176 nasal atr low high round back nasal atr low round high back nasal atr low round back high nasal atr round high low back nasal atr round low high back nasal atr round low back high nasal low atr high round back nasal low atr round high back nasal low atr round back high nasal low round atr high back nasal low round atr back high nasal low round back atr high nasal round atr high low back nasal round atr low high back nasal round atr low back high nasal round low atr high back nasal round low atr back high nasal round low back atr high

177

Appendix F : Results of runs with most frequent underlying sequences

These are the optimal constraint rankings for the most frequent underlying sequences, with the first table representing the single most frequent, the second the two most frequent, and so on. The ranking values are not of specific interest but are provided for the sake of accountability, showing that the runs were clean. The real figure of interest is the average error per candidate. For the single most frequent form, this figure is approximately 8%.

Top 1 Most Frequent (/ Ϫa/) Constraint Ranking Value DEP 106.740 HIGHEST *VV 99.622 RANKING MAX [Ϫ] 98.648 MAX V1 98.648

MAX [ ] 98.024 a LOWEST MAX WORD INITIAL 98.024 RANKING V=N UCLEUS 96.966 NODIPHTHONG 96.966 average error per candidate: 8.241 cycles: 100 000 learning trials: 1 000 000

When the second most frequent form is added, the average error shows a considerable improvement, almost halving. This shows that the benefit of adding data outweighs the potential downside of increased heterogeneity in this instance.

Top 2 Most Frequent (/ Ϫa/, / ea/) Constraint Ranking Value DEP 106.256 HIGHEST *VV 100.298 RANKING MAX [Ϫ] 99.978 V=N UCLEUS 98.260

NODIPHTHONG 98.260

97.762 MAX [e] LOWEST MAX V1 97.740 RANKING MAX [a] 97.446 MAX WORD INITIAL 97.446 average error per candidate: 4.455 cycles: 100 000 learning trials: 1 000 000

The addition of a third form leads to a very slight increase in error. Here the competing forces of data quantity and heterogeneity almost even out.

178

Top 3 Most Frequent (/ Ϫa/, / ea/, / ia/) Constraint Ranking Value DEP 104.092 HIGHEST MAX [Ϫ] 101.544 RANKING *VV 101.270 NODIPHTHONG 99.576

MAX WORD INITIAL 99.060

99.060 MAX [a] MAX [e] 98.516 LOWEST V=N UCLEUS 97.990 RANKING MAX V1 97.588 MAX [i] 97.528 average error per candidate: 4.783 cycles: 100 000 learning trials: 1 000 000

Adding the fourth most frequent form causes a relatively large increase in error, to over 7%. This means that the data for this sequence differ in some meaningful respect(s) from the other data.

Top 4 Most Frequent (/ Ϫa/, / ea/, / ia/, / ϯa/) Constraint Ranking Value DEP 104.248 HIGHEST MAX [Ϫ] 101.256 RANKING *VV 100.524 NODIPHTHONG 100.344

MAX [ ] 100.298 ϯ MAX [e] 99.422 MAX V1 98.780 MAX WORD INITIAL 98.246 MAX [a] 98.246 LOWEST V=N UCLEUS 98.202 RANKING MAX [i] 97.804 average error per candidate: 7.462 cycles: 100 000 learning trials: 1 000 000

When the fifth form is added, the average error per candidate drops. Here the addition of data contributes positively, and there is little or no negative effect of heterogeneity.

179

Top 5 Most Frequent (/ Ϫa/, / ea/, / ia/, / ϯa/, / Ϫϯ/) Constraint Ranking Value DEP 104.700 HIGHEST MAX [Ϫ] 101.838 RANKING *VV 100.808 MAX WORD INITIAL 99.828

MAX [ ] 99.710 e MAX [ϯ] 99.316 NODIPHTHONG 99.222 MAX [i] 98.342 V=N UCLEUS 97.676 LOWEST MAX [a] 97.610 RANKING MAX V1 96.988 average error per candidate: 5.479 cycles: 100 000 learning trials: 1 000 000

Starting with the addition of a sixth form, the overall trend becomes apparent. In general, adding data negatively affects the average error per candidate, meaning that the negatives of data heterogeneity win out against the positives of quantity. The average error per candidate decreases with the addition of the sixth, seventh, eighth, eleventh, fourteenth and fifteenth forms. The other additions (the ninth, tenth, twelfth and thirteenth forms) lower the error rates, but only very slightly in all cases. The general pattern from the top six to the top fifteen most frequent forms is that the average error increases steadily.

Top 6 Most Frequent (/ Ϫa/, / ea/, / ia/, / ϯa/, / Ϫϯ/, / Ϫѐ/) Constraint Ranking Value DEP 104.508 HIGHEST MAX [ѐ] 102.466 RANKING MAX [Ϫ] 102.056

*VV 100.746 MAX WORD INITIAL 100.340 MAX [ϯ] 100.308 NODIPHTHONG 100.002 MAX [e] 99.390

MAX [i] 98.324

V=N UCLEUS 97.800 LOWEST MAX V1 96.606 RANKING MAX [a] 94.402 average error per candidate: 7.070 cycles: 100 000 learning trials: 1 000 000

180

Top 7 Most Frequent (/ Ϫa/, / ea/, / ia/, / ϯa/, / Ϫϯ/, / Ϫѐ/, / Ϫi/) Constraint Ranking Value DEP 106.380 HIGHEST MAX [ѐ] 104.572 RANKING *VV 103.146

MAX [ ] 102.968 Ϫ MAX WORD INITIAL 102.166 NODIPHTHONG 101.946 MAX [ϯ] 101.690 MAX [e] 101.570

MAX [i] 100.882

V=N UCLEUS 98.872 LOWEST MAX V1 89.436 RANKING MAX [a] 79.920 average error per candidate: 7.503 cycles: 100 000 learning trials: 1 000 000

Top 8 Most Frequent (/ Ϫa/, / ea/, / ia/, / ϯa/, / Ϫϯ/, / Ϫѐ/, / Ϫi/, / Ϫœ/) Constraint Ranking Value MAX [œ] 132.000 HIGHEST DEP 105.102 RANKING MAX [ѐ] 103.058

MAX [Ϫ] 101.526 *VV 101.190 NODIPHTHONG 100.762 MAX [e] 100.226 MAX WORD INITIAL 100.192

MAX [ϯ] 100.110

99.434 MAX [i] LOWEST V=N UCLEUS 97.904 RANKING MAX V1 95.612 MAX [a] 59.450 average error per candidate: 7.923 cycles: 100 000 learning trials: 1 000 000

181

Top 9 Most Frequent (/ Ϫa/, / ea/, / ia/, / ϯa/, / Ϫϯ/, / Ϫѐ/, / Ϫi/, / Ϫœ/, / eœ/) Constraint Ranking Value DEP 104.650 HIGHEST MAX [ѐ] 103.412 RANKING MAX [œ] 103.318

MAX [Ϫ] 101.214 *VV 100.690 NODIPHTHONG 100.540 MAX WORD INITIAL 99.864 MAX [e] 99.784

MAX [ϯ] 99.112

98.642 MAX [i] LOWEST MAX V1 97.430 RANKING V=N UCLEUS 97.366 MAX [a] 91.812 average error per candidate: 7.874 cycles: 100 000 learning trials: 1 000 000

Top 10 Most Frequent (/ Ϫa/, / ea/, / ia/, / ϯa/, / Ϫϯ/, / Ϫѐ/, / Ϫi/, / Ϫœ/, / eœ/, / ii/) Constraint Ranking Value DEP 169.000 HIGHEST MAX [œ] 166.838 RANKING MAX [ѐ] 166.154

MAX [Ϫ] 165.302 *VV 164.874 NODIPHTHONG 164.756 MAX WORD INITIAL 164.448 MAX [ϯ] 163.522

MAX [e] 163.218

162.546 MAX [i] LOWEST V=N UCLEUS 162.070 RANKING MAX V1 161.686 MAX [a] -483.524 average error per candidate: 7.605 cycles: 100 000 learning trials: 1 000 000

182

Top 11 Most Frequent (/ Ϫa/, / ea/, / ia/, / ϯa/, / Ϫϯ/, / Ϫѐ/, / Ϫi/, / Ϫœ/, / eœ/, / ii/, / iϪ/) Constraint Ranking Value DEP 105.506 HIGHEST MAX [œ] 103.894 RANKING MAX [ѐ] 103.150

*VV 101.964 MAX WORD INITIAL 100.850 MAX V1 100.700 MAX [Ϫ] 100.550 NODIPHTHONG 100.548 V=N UCLEUS 98.732 MAX [ϯ] 98.550

MAX [i] 98.170 LOWEST RANKING MAX [e] 95.042 MAX [a] 94.442 average error per candidate: 8.616 cycles: 100 000 learning trials: 1 000 000

Top 12 Most Frequent (/ Ϫa/, / ea/, / ia/, / ϯa/, / Ϫϯ/, / Ϫѐ/, / Ϫi/, / Ϫœ/, / eœ/, / ii/, / iϪ/, / ei/) Constraint Ranking Value DEP 104.044 HIGHEST MAX [ѐ] 102.818 RANKING MAX [œ] 102.204

MAX [Ϫ] 99.500 MAX [a] 99.500 *VV 99.444 MAX [e] 99.432

NODIPHTHONG 98.854

98.770 MAX [ϯ] MAX [i] 97.322 LOWEST V=N UCLEUS 96.966 RANKING MAX V1 96.788 MAX WORD INITIAL 95.888 average error per candidate: 8.501 cycles: 100 000 learning trials: 1 000 000

183

Top 13 Most Frequent (/ Ϫa/, / ea/, / ia/, / ϯa/, / Ϫϯ/, / Ϫѐ/, / Ϫi/, / Ϫœ/, / eœ/, / ii/, / iϪ/, / ei/, / ao/) Constraint Ranking Value MAX [o] 140.000 HIGHEST DEP 100.504 RANKING MAX [œ] 98.804

MAX [ѐ] 98.244 MAX [Ϫ] 95.898 *VV 95.838 NODIPHTHONG 95.446

MAX [ϯ] 95.414

94.666 MAX [e] MAX WORD INITIAL 93.702 MAX [i] 93.698 LOWEST MAX [a] 93.520 RANKING V=N UCLEUS 93.414 MAX V1 92.084 average error per candidate: 7.991 cycles: 100 000 learning trials: 1 000 000

Top 14 Most Frequent (/ Ϫa/, / ea/, / ia/, / ϯa/, / Ϫϯ/, / Ϫѐ/, / Ϫi/, / Ϫœ/, / eœ/, / ii/, / iϪ/, / ei/, / ao/, /eѐ/) Constraint Ranking Value MAX [o] 128.000 HIGHEST DEP 101.356 RANKING MAX [ѐ] 100.386

MAX [œ] 99.902 *VV 97.504 MAX [Ϫ] 96.996 MAX [e] 96.766

NODIPHTHONG 96.652

96.502 MAX [ϯ] MAX WORD INITIAL 95.796 MAX V1 95.016 LOWEST V=N UCLEUS 94.404 RANKING MAX [a] 94.112 MAX [i] 94.072 average error per candidate: 9.211 cycles: 100 000 learning trials: 1 000 000

184

Top 15 Most Frequent (/ Ϫa/, / ea/, / ia/, / ϯa/, / Ϫϯ/, / Ϫѐ/, / Ϫi/, / Ϫœ/, / eœ/, / ii/, / iϪ/, / ei/, / ao/, /eѐ/, / ie/) Constraint Ranking Value MAX [o] 130.000 HIGHEST DEP 101.300 RANKING MAX [œ] 100.360

MAX [ѐ] 99.404 *VV 97.534 MAX [e] 97.200 NODIPHTHONG 97.028

MAX [Ϫ] 96.916

MAX WORD INITIAL 95.680

MAX V1 95.402

95.048 MAX [i] LOWEST MAX [ϯ] 94.942 RANKING V=N UCLEUS 94.180 MAX [a] 93.116 average error per candidate: 9.480 cycles: 100 000 learning trials: 1 000 000

185

Appendix G : Input vs. Generated Frequencies for Best Constraint Set For every underlying form (given in two columns in order of frequency and provided at the top left), each candidate is listed with its frequency in the data (input frequency), the frequency generated by OTSoft (generated frequency), and the number of forms generated by OTSoft (generated number). (G = glide ([ j], [ w] or [ і]))

/Ϫa/ Input Fr. Gen Fr. Gen. # /Ϫi/ Input Fr. Gen Fr. Gen. # Ϫ 0.420 0.288 28803 Ϫj 0.900 0.491 49083 a 0.080 0.108 10837 i 0.000 0.063 6267 Ϫa 0.160 0.379 37892 Ϫ 0.000 0.181 18077 Ϫ⍝a 0.340 0.207 20658 Ϫi 0.100 0.256 25559 ϪGa 0.000 0.018 1810 ϪGi 0.000 0.010 1014

/ea/ Input Fr. Gen Fr. Gen. # /Ϫœ/ Input Fr. Gen Fr. Gen. # a 0.314 0.222 22153 œ 0.600 0.212 21178 e 0.257 0.222 22226 Ϫ 0.000 0.025 2548 ea 0.171 0.352 35227 Ϫœ 0.350 0.464 46419 e⍝a 0.257 0.188 18783 Ϫ⍝œ 0.050 0.272 27220 eGa 0.000 0.016 1611 ϪGœ 0.000 0.026 2635

/ia/ Input Fr. Gen Fr. Gen. # /eœ/ Input Fr. Gen Fr. Gen. # ja 0.563 0.465 46537 œ 0.474 0.333 33279 a 0.250 0.142 14241 e 0.053 0.015 1510 i 0.031 0.143 14295 eœ 0.316 0.404 40446 ia 0.125 0.240 24004 e⍝œ 0.158 0.227 22670 iGa 0.031 0.009 923 eGœ 0.000 0.021 2095

/ϯa/ Input Fr. Gen Fr. Gen. # /ii/ Input Fr. Gen Fr. Gen. # ϯ 0.300 0.222 22226 i 0.421 0.395 39525 a 0.267 0.222 22153 ii 0.158 0.200 20042 ϯa 0.300 0.352 35227 ij/ji 0.421 0.397 39678 ϯ⍝a 0.133 0.188 18783 iGi 0.000 0.008 755 ϯGa 0.000 0.016 1611 /iϪ/ Input Fr. Gen Fr. Gen. # /Ϫϯ/ Input Fr. Gen Fr. Gen. # jϪ 0.556 0.491 49061 Ϫ⍝ϯ 0.600 0.207 20658 Ϫ 0.111 0.180 18025 ϯ 0.040 0.108 10837 i 0.222 0.063 6297 Ϫ 0.240 0.288 28803 iϪ 0.111 0.256 25591 Ϫϯ 0.120 0.379 37892 iGϪ 0.000 0.010 1026

ϪGϯ 0.000 0.018 1810 /ei/ Input Fr. Gen Fr. Gen. # /Ϫѐ/ Input Fr. Gen Fr. Gen. # e 0.438 0.143 14295 Ϫѐ 0.524 0.404 40446 i 0.063 0.142 14241 ѐ 0.333 0.333 33279 ei 0.063 0.240 24004 Ϫ 0.048 0.015 1510 ej 0.438 0.465 46537 Ϫ⍝ѐ 0.095 0.227 22670 eGi 0.000 0.009 923

ϪGѐ 0.000 0.021 2095

186

/ao/ Input Fr. Gen Fr. Gen. # /ϯѐ/ Input Fr. Gen Fr. Gen. # o 0.800 0.287 28657 ѐ 0.545 0.335 33513 a 0.000 0.110 10952 ϯ 0.000 0.010 1019 ao 0.133 0.380 37950 ϯѐ 0.455 0.406 40587 a⍝o 0.067 0.206 20617 ϯ⍝ѐ 0.000 0.228 22769 aGo 0.000 0.018 1824 ϯGѐ 0.000 0.021 2112

/eѐ/ Input Fr. Gen Fr. Gen. # /ϯa/ Input Fr. Gen Fr. Gen. # eѐ 0.571 0.406 40587 a 0.636 0.333 33279 ѐ 0.000 0.335 33513 ϯ 0.000 0.015 1510 e 0.000 0.010 1019 ϯa 0.091 0.404 40446 e⍝ѐ 0.429 0.228 22769 ϯ⍝a 0.182 0.227 22670 eGѐ 0.000 0.021 2112 ϯGa 0.091 0.021 2095

/ie/ Input Fr. Gen Fr. Gen. # /e௙/ Input Fr. Gen Fr. Gen. # je 0.786 0.465 46537 ௙ 0.455 0.222 22153 e 0.071 0.142 14241 e 0.455 0.222 22226 i 0.071 0.143 14295 e௙ 0.000 0.352 35227 ie 0.071 0.240 24004 e⍝௙ 0.091 0.188 18783 iGe 0.000 0.009 923 eG௙ 0.000 0.016 1611

/iѐ/ Input Fr. Gen Fr. Gen. # /Ϫo/ Input Fr. Gen Fr. Gen. # jѐ 0.429 0.510 51038 o 0.333 0.222 22153 ѐ 0.214 0.205 20451 Ϫ 0.111 0.222 22226 i 0.000 0.005 470 Ϫo 0.333 0.352 35227 iѐ 0.214 0.269 26877 Ϫ⍝o 0.222 0.188 18783 iGѐ 0.143 0.012 1164 ϪGo 0.000 0.016 1611

/Ϫy/ Input Fr. Gen Fr. Gen. # /ϯœ/ Input Fr. Gen Fr. Gen. # Ϫy 0.583 0.256 25559 ϯœ 0.556 0.404 40446 y 0.000 0.063 6267 œ 0.222 0.333 33279 Ϫ 0.000 0.181 18077 ϯ 0.000 0.015 1510 Ϫі 0.333 0.491 49083 ϯ⍝œ 0.222 0.227 22670 ϪGy 0.083 0.010 1014 ϯGœ 0.000 0.021 2095

/aa/ Input Fr. Gen Fr. Gen. # /Ϫ௙/ Input Fr. Gen Fr. Gen. # a 0.667 0.545 54523 Ϫ 0.500 0.288 28803 aa 0.333 0.291 29117 ௙ 0.250 0.108 10837 a⍝a 0.000 0.151 15063 Ϫ௙ 0.250 0.379 37892 aGa 0.000 0.013 1297 Ϫ⍝௙ 0.000 0.207 20658 ϪG௙ 0.000 0.018 1810 /eϪ/ Input Fr. Gen Fr. Gen. # e⍝Ϫ 0.500 0.206 20617 /Ϫe/ Input Fr. Gen Fr. Gen. # Ϫ 0.167 0.287 28657 Ϫ⍝e 0.500 0.207 20658 e 0.000 0.110 10952 e 0.250 0.108 10837 eϪ 0.333 0.380 37950 Ϫ 0.250 0.288 28803 eGϪ 0.000 0.018 1824 Ϫe 0.000 0.379 37892 G 0.000 0.018 1810 Ϫ e

187

/Ϫa/ Input Fr. Gen Fr. Gen. # /ϯ௙/ Input Fr. Gen Fr. Gen. # Ϫa 0.625 0.464 46419 ϯ 0.571 0.222 22226 a 0.250 0.212 21178 ௙ 0.286 0.222 22153 Ϫ 0.000 0.025 2548 ϯ௙ 0.143 0.352 35227 Ϫ⍝a 0.125 0.272 27220 ϯ⍝௙ 0.000 0.188 18783 ϪGa 0.000 0.026 2635 ϯG௙ 0.000 0.016 1611

/ϯi/ Input Fr. Gen Fr. Gen. # /eϯ/ Input Fr. Gen Fr. Gen. # ϯj 0.500 0.465 46537 e⍝ϯ 0.429 0.188 18783 i 0.000 0.142 14241 ϯ 0.286 0.222 22153 ϯ 0.000 0.143 14295 e 0.143 0.222 22226 ϯi 0.375 0.240 24004 eϯ 0.143 0.352 35227 ϯGi 0.125 0.009 923 eGϯ 0.000 0.016 1611

/aϪ/ Input Fr. Gen Fr. Gen. # /ee/ Input Fr. Gen Fr. Gen. # a 0.875 0.110 10952 e 1.000 0.545 54523 Ϫ 0.000 0.287 28657 ee 0.000 0.291 29117 aϪ 0.125 0.380 37950 e⍝e 0.000 0.151 15063 a⍝Ϫ 0.000 0.206 20617 eGe 0.000 0.013 1297 aGϪ 0.000 0.018 1824 /ϯe/ Input Fr. Gen Fr. Gen. # /eo/ Input Fr. Gen Fr. Gen. # ϯ 0.667 0.222 22226 eo 0.500 0.380 37950 e 0.167 0.222 22153 o 0.250 0.287 28657 ϯe 0.167 0.352 35227 e 0.000 0.110 10952 ϯ⍝e 0.000 0.188 18783 e⍝o 0.250 0.206 20617 ϯGe 0.000 0.016 1611 eGo 0.000 0.018 1824 /aœ/ Input Fr. Gen Fr. Gen. # /ea/ Input Fr. Gen Fr. Gen. # aœ 0.500 0.404 40446 a 0.375 0.333 33279 œ 0.167 0.333 33279 e 0.000 0.015 1510 a 0.000 0.015 1510 ea 0.250 0.404 40446 a⍝œ 0.333 0.227 22670 e⍝a 0.375 0.227 22670 aGœ 0.000 0.021 2095 eGa 0.000 0.021 2095 /io/ Input Fr. Gen Fr. Gen. # /ia/ Input Fr. Gen Fr. Gen. # i 0.500 0.063 6297 ia 0.375 0.268 26814 o 0.167 0.180 18025 a 0.250 0.203 20330 io 0.000 0.256 25591 i 0.000 0.007 733 jo 0.333 0.491 49061 ja 0.250 0.510 50965 iGo 0.000 0.010 1026 iGa 0.125 0.012 1158 /aa/ Input Fr. Gen Fr. Gen. # /ѐa/ Input Fr. Gen Fr. Gen. # aa 1.000 0.405 40504 ѐa 0.625 0.407 40651 a 0.000 0.015 1521 a 0.000 0.010 1015 a 0.000 0.333 33343 ѐa 0.250 0.336 33578 a⍝a 0.000 0.226 22559 ѐ⍝a 0.125 0.227 22661 aGa 0.000 0.021 2073 G 0.000 0.021 2095 ѐ a

188

/aѐ/ Input Fr. Gen Fr. Gen. # /ya/ Input Fr. Gen Fr. Gen. # a⍝ѐ 0.500 0.206 20617 a 0.400 0.142 14241 ѐ 0.167 0.287 28657 y 0.000 0.143 14295 a 0.000 0.110 10952 ya 0.400 0.240 24004 aѐ 0.333 0.380 37950 іa 0.200 0.465 46537 aGѐ 0.000 0.018 1824 yGa 0.000 0.009 923

/ϪϪ/ Input Fr. Gen Fr. Gen. # /ai/ Input Fr. Gen Fr. Gen. # ϪϪ 0.800 0.291 29117 aj 0.800 0.509 50867 Ϫ 0.200 0.545 54523 i 0.000 0.008 776 Ϫ⍝Ϫ 0.000 0.151 15063 a 0.000 0.204 20366 ϪGϪ 0.000 0.013 1297 ai 0.200 0.269 26875 aGi 0.000 0.011 1116 /ϯo/ Input Fr. Gen Fr. Gen. # o 0.400 0.287 28657 /ϯϪ/ Input Fr. Gen Fr. Gen. # ϯ 0.000 0.110 10952 ϯϪ 0.500 0.380 37950 ϯo 0.200 0.380 37950 Ϫ 0.250 0.287 28657 ϯ⍝o 0.400 0.206 20617 ϯ 0.000 0.110 10952 ϯGo 0.000 0.018 1824 ϯ⍝Ϫ 0.250 0.206 20617 ϯGϪ 0.000 0.018 1824 /ey/ Input Fr. Gen Fr. Gen. # y 0.400 0.142 14241 /ϯa/ Input Fr. Gen Fr. Gen. # e 0.000 0.143 14295 ϯ 0.750 0.333 33343 ey 0.400 0.240 24004 a 0.000 0.015 1521 eі 0.200 0.465 46537 ϯa 0.000 0.405 40504 eGy 0.000 0.009 923 ϯ⍝a 0.250 0.226 22559 ϯGa 0.000 0.021 2073 /i௙/ Input Fr. Gen Fr. Gen. # ௙ 0.800 0.142 14241 /ѐѐ/ Input Fr. Gen Fr. Gen. # i 0.000 0.143 14295 ѐѐ 1.000 0.291 29117 i௙ 0.200 0.240 24004 ѐ 0.000 0.545 54523 j௙ 0.000 0.465 46537 ѐ⍝ѐ 0.000 0.151 15063 iG௙ 0.000 0.009 923 ѐGѐ 0.000 0.013 1297

/iœ/ Input Fr. Gen Fr. Gen. # /a௙/ Input Fr. Gen Fr. Gen. # œ 0.400 0.203 20330 a௙ 0.500 0.405 40504 i 0.000 0.007 733 ௙ 0.000 0.015 1521 iœ 0.400 0.268 26814 a 0.250 0.333 33343 jœ 0.200 0.510 50965 a⍝௙ 0.250 0.226 22559 iGœ 0.000 0.012 1158 aG௙ 0.000 0.021 2073

/oa/ Input Fr. Gen Fr. Gen. # /Ϫu/ Input Fr. Gen Fr. Gen. # oGa 0.600 0.018 1810 Ϫu 0.667 0.240 24004 a 0.000 0.108 10837 u 0.000 0.142 14241 o 0.000 0.288 28803 Ϫ 0.000 0.143 14295 oa 0.400 0.379 37892 Ϫw 0.333 0.465 46537 o⍝a 0.000 0.207 20658 ϪGu 0.000 0.009 923

189

/ϯϯ/ Input Fr. Gen Fr. Gen. # /iœ/ Input Fr. Gen Fr. Gen. # ϯ 1.000 0.545 54523 jœ 1.000 0.465 46537 ϯϯ 0.000 0.291 29117 œ 0.000 0.142 14241 ϯ⍝ϯ 0.000 0.151 15063 i 0.000 0.143 14295 ϯGϯ 0.000 0.013 1297 iœ 0.000 0.240 24004 iGœ 0.000 0.009 923 /ϯy/ Input Fr. Gen Fr. Gen. # ϯy 0.667 0.240 24004 /ϯ௙/ Input Fr. Gen Fr. Gen. # y 0.000 0.142 14241 ϯ 0.667 0.333 33343 ϯ 0.000 0.143 14295 ௙ 0.000 0.015 1521 ϯі 0.000 0.465 46537 ϯ௙ 0.000 0.405 40504 ϯGy 0.333 0.009 923 ϯ⍝௙ 0.333 0.226 22559 ϯG௙ 0.000 0.021 2073 /aѐ/ Input Fr. Gen Fr. Gen. # ѐ 0.333 0.287 28657 /ϯi/ Input Fr. Gen Fr. Gen. # a 0.333 0.110 10952 ϯj 0.667 0.509 50867 aѐ 0.333 0.380 37950 i 0.000 0.008 776 a⍝ѐ 0.000 0.206 20617 ϯ 0.333 0.204 20366 aGѐ 0.000 0.018 1824 ϯi 0.000 0.269 26875 ϯGi 0.000 0.011 1116 /ae/ Input Fr. Gen Fr. Gen. # e 0.333 0.222 22153 /ϯa/ Input Fr. Gen Fr. Gen. # a 0.000 0.222 22226 ϯa 0.667 0.352 35227 ae 0.333 0.352 35227 a 0.333 0.222 22153 a⍝e 0.333 0.188 18783 ϯ 0.000 0.222 22226 aGe 0.000 0.016 1611 ϯ⍝a 0.000 0.188 18783 ϯGa 0.000 0.016 1611 /ai/ Input Fr. Gen Fr. Gen. # aj 1.000 0.465 46537 /o௙/ Input Fr. Gen Fr. Gen. # i 0.000 0.142 14241 o 0.667 0.288 28803 a 0.000 0.143 14295 ௙ 0.000 0.108 10837 ai 0.000 0.240 24004 o௙ 0.333 0.379 37892 aGi 0.000 0.009 923 o⍝௙ 0.000 0.207 20658 oG௙ 0.000 0.018 1810 /iϯ/ Input Fr. Gen Fr. Gen. # jϯ 0.667 0.465 46537 /y௙/ Input Fr. Gen Fr. Gen. # ϯ 0.000 0.142 14241 ௙ 0.333 0.142 14241 i 0.000 0.143 14295 y 0.333 0.143 14295 iϯ 0.333 0.240 24004 y௙ 0.333 0.240 24004 iGϯ 0.000 0.009 923 і௙ 0.000 0.465 46537 yG௙ 0.000 0.009 923 /iѐ/ Input Fr. Gen Fr. Gen. # ѐ 0.333 0.180 18025 /yo/ Input Fr. Gen Fr. Gen. # i 0.000 0.063 6297 yo 0.667 0.256 25591 iѐ 0.333 0.256 25591 o 0.333 0.180 18025 jѐ 0.333 0.491 49061 y 0.000 0.063 6297 iGѐ 0.000 0.010 1026 іo 0.000 0.491 49061 G 0.000 0.010 1026 y o

190

/ya/ Input Fr. Gen Fr. Gen. # /Ϫϯ/ Input Fr. Gen Fr. Gen. # y 0.333 0.007 733 Ϫ 0.500 0.025 2548 a 0.000 0.203 20330 ϯ 0.000 0.212 21178 ya 0.333 0.268 26814 Ϫϯ 0.000 0.464 46419 іa 0.333 0.510 50965 Ϫ⍝ϯ 0.500 0.272 27220 yGa 0.000 0.012 1158 ϪGϯ 0.000 0.026 2635

/œa/ Input Fr. Gen Fr. Gen. # /ϯѐ/ Input Fr. Gen Fr. Gen. # œ⍝a 0.667 0.226 22559 ϯѐ 1.000 0.380 37950 a 0.000 0.015 1521 ѐ 0.000 0.287 28657 œ 0.000 0.333 33343 ϯ 0.000 0.110 10952 œa 0.333 0.405 40504 ϯ⍝ѐ 0.000 0.206 20617 œGa 0.000 0.021 2073 ϯGѐ 0.000 0.018 1824

/ѐϯ/ Input Fr. Gen Fr. Gen. # /ϯϯ/ Input Fr. Gen Fr. Gen. # ѐϯ 0.667 0.407 40651 ϯ 0.500 0.333 33279 ϯ 0.000 0.010 1015 ϯ 0.000 0.015 1510 ѐ 0.333 0.336 33578 ϯϯ 0.500 0.404 40446 ѐ⍝ϯ 0.000 0.227 22661 ϯ⍝ϯ 0.000 0.227 22670 ѐGϯ 0.000 0.021 2095 ϯGϯ 0.000 0.021 2095

/ѐi/ Input Fr. Gen Fr. Gen. # /ѐi/ Input Fr. Gen Fr. Gen. # ѐj 0.667 0.510 50967 ѐj 1.000 0.491 49083 i 0.000 0.005 464 i 0.000 0.063 6267 ѐ 0.000 0.205 20489 ѐ 0.000 0.181 18077 ѐi 0.333 0.270 26956 ѐi 0.000 0.256 25559 ѐGi 0.000 0.011 1124 ѐGi 0.000 0.010 1014

/ѐa/ Input Fr. Gen Fr. Gen. # /eѐ/ Input Fr. Gen Fr. Gen. # ѐa 0.667 0.379 37892 ѐ 0.500 0.287 28657 a 0.333 0.108 10837 e 0.000 0.110 10952 ѐ 0.000 0.288 28803 eѐ 0.500 0.380 37950 ѐ⍝a 0.000 0.207 20658 e⍝ѐ 0.000 0.206 20617 ѐGa 0.000 0.018 1810 eGѐ 0.000 0.018 1824

/aœ/ Input Fr. Gen Fr. Gen. # /eu/ Input Fr. Gen Fr. Gen. # œ 0.333 0.222 22153 u 0.500 0.180 18025 a 0.333 0.222 22226 e 0.000 0.063 6297 aœ 0.333 0.352 35227 eu 0.000 0.256 25591 a⍝œ 0.000 0.188 18783 ew 0.500 0.491 49061 aGœ 0.000 0.016 1611 eGu 0.000 0.010 1026

/øѐ/ Input Fr. Gen Fr. Gen. # /iu/ Input Fr. Gen Fr. Gen. # øѐ 0.667 0.406 40587 iu 0.500 0.256 25591 ѐ 0.000 0.335 33513 u 0.000 0.180 18025 ø 0.000 0.010 1019 i 0.000 0.063 6297 ø⍝ѐ 0.333 0.228 22769 iw/ju 0.500 0.491 49061 øGѐ 0.000 0.021 2112 iGu 0.000 0.010 1026

191

/ϯϪ/ Input Fr. Gen Fr. Gen. # /yi/ Input Fr. Gen Fr. Gen. # ϯ 0.500 0.212 21212 y 1.000 0.143 14295 Ϫ 0.000 0.026 2558 i 0.000 0.142 14241 ϯϪ 0.000 0.465 46535 yi 0.000 0.240 24004 ϯ⍝Ϫ 0.500 0.271 27068 іi/yj 0.000 0.465 46537 ϯGϪ 0.000 0.026 2627 yGi 0.000 0.009 923

/ϯѐ/ Input Fr. Gen Fr. Gen. # /yϯ/ Input Fr. Gen Fr. Gen. # ϯѐ 0.500 0.465 46535 ϯ 0.500 0.203 20330 ѐ 0.000 0.026 2558 y 0.000 0.007 733 ϯ 0.000 0.212 21212 yϯ 0.000 0.268 26814 ϯ⍝ѐ 0.500 0.271 27068 іϯ 0.500 0.510 50965 ϯGѐ 0.000 0.026 2627 yGϯ 0.000 0.012 1158

/ϯo/ Input Fr. Gen Fr. Gen. # /ao/ Input Fr. Gen Fr. Gen. # ϯo 0.500 0.465 46535 ao 1.000 0.465 46535 o 0.000 0.026 2558 o 0.000 0.026 2558 ϯ 0.000 0.212 21212 a 0.000 0.212 21212 ϯ⍝o 0.500 0.271 27068 a⍝o 0.000 0.271 27068 ϯGo 0.000 0.026 2627 aGo 0.000 0.026 2627

/ϯu/ Input Fr. Gen Fr. Gen. # /aa/ Input Fr. Gen Fr. Gen. # ϯu 1.000 0.301 30096 aa 1.000 0.291 29117 u 0.000 0.013 1256 a 0.000 0.545 54523 ϯ 0.000 0.114 11439 a⍝a 0.000 0.151 15063 ϯw 0.000 0.558 55838 aGa 0.000 0.013 1297 ϯGu 0.000 0.014 1371 /øϯ/ Input Fr. Gen Fr. Gen. # /ϯѐ/ Input Fr. Gen Fr. Gen. # ϯ 0.500 0.222 22153 ϯѐ 0.500 0.380 37950 ø 0.000 0.222 22226 ѐ 0.000 0.287 28657 øϯ 0.500 0.352 35227 ϯ 0.000 0.110 10952 ø⍝ϯ 0.000 0.188 18783 ϯ⍝ѐ 0.500 0.206 20617 øGϯ 0.000 0.016 1611 ϯGѐ 0.000 0.018 1824 /øϯ/ Input Fr. Gen Fr. Gen. # /ua/ Input Fr. Gen Fr. Gen. # øϯ 1.000 0.404 40446 ua 0.500 0.300 30003 ϯ 0.000 0.333 33279 a 0.000 0.114 11423 ø 0.000 0.015 1510 u 0.000 0.012 1215 ø⍝ϯ 0.000 0.227 22670 wa 0.000 0.560 55958 øGϯ 0.000 0.021 2095 uGa 0.500 0.014 1401 /øa/ Input Fr. Gen Fr. Gen. # /yϯ/ Input Fr. Gen Fr. Gen. # ø⍝a 0.500 0.227 22670 ϯ 1.000 0.142 14241 a 0.000 0.333 33279 y 0.000 0.143 14295 ø 0.000 0.015 1510 yϯ 0.000 0.240 24004 øa 0.000 0.404 40446 іϯ 0.000 0.465 46537 ø G a 0.500 0.021 2095 yGϯ 0.000 0.009 923

192

/œø/ Input Fr. Gen Fr. Gen. # œø 1.000 0.352 35227 ø 0.000 0.222 22153 œ 0.000 0.222 22226 œ⍝ø 0.000 0.188 18783

œGø 0.000 0.016 1611

193

Appendix H : Summary of Grammars Tested with GLA in OTSoft

The grammars submitted to OTSoft are listed according to the constraints that were tested. The ways in which the runs were restricted or altered are listed under Restrictions, Modifications, in particular when they serve to distinguish otherwise identical runs. The last column provides the average error per candidate for clean runs. Runs that were not clean (suggesting they involve harmonic bounding) are listed as invalid.

If Valid Grammar, Constraints Restrictions, Modifications Average Error per Candidate DEP, MAX[+ATR], MAX[+back], -10 most frequent forms only MAX[+high], MAX[+low], MAX[+nasal], -MAX constraints = strong MAX[+round], MAXRootNode, MAXV1, invalid -hierarchy: ATR>high>low> MAXWordInitial, NoDiphthong, round>back>nasal V=nucleus, *VV DEP, MAX[+ATR], MAX[+back], -10 most frequent forms only MAX[+high], MAX[+low], MAX[+nasal], -MAX constraints = strong MAX[+round], MAXRootNode, invalid -hierarchy: ATR>high>low> MAXWordInitial, NoDiphthong, round>back>nasal V=nucleus, *VV DEP, MAX[+ATR], MAX[+back], MAX[+high], MAX[+low], MAX[+nasal], -Singleton underlying forms MAX[-round], MAXLexical, excluded 18.495% MAXMonosegmental, MAXRootNode, MAXV1, -MAX constraints = strong MAXWordInitial, NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[+back], MAX[+high], MAX[+low], MAX[+nasal], -Singleton underlying forms MAX[-round], MAXLexical, excluded 18.581% MAXMonosegmental, MAXV1, -MAX constraints = strong MAXWordInitial, NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[+back], MAX[+high], MAX[+low], MAX[+nasal], -Singleton underlying forms MAX[-round], MAXLexical, excluded 18.599% MAXMonosegmental, MAXV1, -MAX constraints = strong MAXWordInitial, NoDiphthong, -il/ils/il y tokens excluded V=nucleus, *VV DEP, MAX[+ATR], MAX[+back], -5 most frequent forms only MAX[+high], MAX[-low], MAXRootNode, -MAX constraints = weak invalid MAXWordInitial, NoDiphthong, -il/ils/il y tokens excluded V=nucleus, *VV -Singleton underlying forms DEP, MAX[+ATR], MAX[+back], MAX[+low], excluded MAX[+nasal], MAX[+round], MAXRootNode, -MAX constraints = weak 16.2% MAXV1, MAXWordInitial, NoDiphthong, -hierarchy: ATR>high>low> V=nucleus, *VV round>back>nasal -Singleton underlying forms DEP, MAX[+ATR], MAX[+back], excluded MAX[+nasal], MAX[+round], MAXRootNode, -MAX constraints = weak invalid MAXV1, MAXWordInitial, NoDiphthong, -hierarchy: ATR>high>low> V=nucleus, *VV round>back>nasal DEP, MAX[+ATR], MAX[+back], MAX[-low], -5 most frequent forms only MAXRootNode, MAXWordInitial, -MAX constraints = weak invalid NoDiphthong, V=nucleus, *VV -il/ils/il y tokens excluded

194

DEP, MAX[+ATR], MAX[-ATR], MAX[+back], MAX[-back], MAX[+high], MAX[-high], MAX[+low], MAX[-low], MAX[+nasal], -Singleton underlying forms MAX[-nasal], MAX[+round], MAX[-round], excluded 18.607% MAXLexical, MAXMonosegmental, -MAX constraints = weak MAXRootNode, MAXV1, MAXWordInitial, NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], MAX[-back], MAX[+high], MAX[-high], -Singleton underlying forms MAX[+low], MAX[-low], MAX[+nasal], excluded 18.468% MAX[-nasal], MAX[+round], MAX[-round], -MAX constraints = strong MAXLexical, MAXMonosegmental, MAXV1, MAXWordInitial, NoDiphthong, V=nucleus DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -10 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = weak MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>ATR>high> MAXRootNode, MAXV1, MAXWordInitial, back>nasal>round NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -10 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = weak MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>ATR>high> MAXRootNode, MAXV1, MAXWordInitial, back>round>nasal NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -10 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = weak MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>ATR>high> MAXRootNode, MAXV1, MAXWordInitial, round>nasal>back NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -10 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = weak MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded invalid MAX[-nasal], MAX[+round], MAX[-round], -hierar chy: low>ATR>high> MAXRootNode, MAXV1, MAXWordInitial, round>back>nasal NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -10 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = weak MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: nasal>low>ATR> MAXRootNode, MAXV1, MAXWordInitial, high>back>round NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -10 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = weak MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: nasal>low>ATR> MAXRootNode, MAXV1, MAXWordInitial, high>round>back NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -5 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = weak MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded 4.545% MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>ATR>high> MAXRootNode, MAXV1, MAXWordInitial, round>back>nasal NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -5 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = weak MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded 7.553% MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: nasal>low>ATR> MAXRootNode, MAXV1, MAXWordInitial, high>back>round NoDiphthong, V=nucleus, *VV

195

DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -8 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = weak MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>ATR>high> MAXRootNode, MAXV1, MAXWordInitial, nasal>back>round NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = strong invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>ATR>high> MAXRootNode, MAXV1, MAXWordInitial, nasal>back>round NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = weak invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>ATR>high> MAXRootNode, MAXV1, MAXWordInitial, back>nasal>round NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = weak invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: ATR>high>low> MAXRootNode, MAXV1, MAXWordInitial, round>back>nasal NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = weak invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>ATR>high> MAXRootNode, MAXV1, MAXWordInitial, nasal>back>round NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = weak invalid MAX[-nasal], MAX[+round], MAX[-round], -il/ils/il y tokens excluded MAXRootNode, MAXV1, MAXWordInitial, -hierarchy: low>ATR>high> NoDiphthong, V=nucleus, *VV nasal>back>round DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = weak invalid MAX[-nasal], MAX[+round], MAX[-round], -il/ils/il y tokens excluded MAXRootNode, MAXV1, MAXWordInitial, -hierarchy: low>ATR>high> NoDiphthong, V=nucleus, *VV nasal>round>back DEP, MAX[+ATR], MAX[-ATR], MAX[+back], MAX[-back], MAX[+high], MAX[-high], -5 most frequent forms only MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = strong 5.742% MAX[-nasal], MAX[+round], MAX[-round], -il/ils/il y tokens excluded MAXRootNode, MAXWordInitial, -contrastive specifications NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -5 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = weak MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded 4.858% MAX[-nasal], MAX[+round], MAX[-round], -contrastive specifications MAXRootNode, MAXWordInitial, -ATR>low NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -5 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = weak MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded 4.62% MAX[-nasal], MAX[+round], MAX[-round], -contrastive specifications MAXRootNode, MAXWordInitial, -low>ATR NoDiphthong, V=nucleus, *VV

196

DEP, MAX[+ATR], MAX[-ATR], MAX[+back], MAX[-back], MAX[+high], MAX[-high], -5 most frequent forms only MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = weak 4.954% MAX[-nasal], MAX[+round], MAX[-round], -il/ils/il y tokens excluded MAXRootNode, MAXWordInitial, -full specifications NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = strong invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>ATR>high> MAXRootNode, MAXWordInitial, nasal>back>round NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = weak invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>ATR>high> MAXRootNode, MAXWordInitial, back>nasal>round NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = weak invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: ATR>high>low> MAXRootNode, MAXWordInitial, round>back>nasal NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = weak invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>ATR>high> MAXRootNode, MAXWordInitial, nasal>back>round NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], MAX[-back], MAX[+high], MAX[-high], -Singleton underlying forms MAX[+low], MAX[-low], MAX[+nasal], excluded 16.335% MAX[-nasal], MAX[+round], MAX[-round], -MAX constraints = strong MAXRootNode, MAXWordInitial, -il/ils/il y tokens excluded NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -10 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = weak MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>ATR>high> MAXRootNode, NoDiphthong, V=nucleus, back>nasal>round *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -10 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = weak MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>ATR>high> MAXRootNode, NoDiphthong, V=nucleus, back>round>nasal *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -10 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = weak MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>ATR>high> MAXRootNode, NoDiphthong, V=nucleus, round>nasal>back *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -10 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = weak MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: nasal>low>ATR> MAXRootNode, NoDiphthong, V=nucleus, high>back>round *VV

197

DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = strong invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>A TR>high> MAXRootNode, NoDiphthong, V=nucleus, nasal>back>round *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = weak invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>ATR>high> MAXRootNode, NoDiphthong, V=nucleus, back>nasal>round *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = weak invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: ATR>high>low> MAXRootNode, NoDiphthong, V=nucleus, round>back>nasal *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = weak invalid MAX[-nasal], MAX[+round], MAX[-round], -hierarchy: low>ATR>high> MAXRootNode, NoDiphthong, V=nucleus, nasal>back>round *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = weak 16.785% MAX[-nasal], MAX[+round], MAX[-round], -il/ils/il y tokens excluded MAXRootNode, NoDiphthong, V=nucleus, -hierarchy: low> ATR>high> *VV nasal>round>back DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = weak invalid MAX[-nasal], MAX[+round], MAX[-round], -il/ils/il y tokens excluded MAXRootNode, NoDiphthong, V=nucleus, -hierarchy: low>ATR>high> *VV round>back>nasal DEP, MAX[+ATR], MAX[-ATR], MAX[+back], MAX[-back], MAX[+high], MAX[-high], -5 most frequent forms only MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = weak invalid MAX[-nasal], MAX[+round], MAX[-round], -il/ils/il y tokens excluded MAXV1, MAXWordInitial, NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], MAX[-back], MAX[+high], MAX[-high], -Singleton underlying forms MAX[+low], MAX[-low], MAX[+nasal], excluded 18.731% MAX[-nasal], MAX[+round], MAX[-round], -MAX constraints = strong MAXV1, MAXWordInitial, NoDiphthong, -full feature specifications V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], MAX[-back], MAX[+high], MAX[-high], -Singleton underlying forms MAX[+low], MAX[-low], MAX[+nasal], excluded 16.253% MAX[-nasal], MAX[+round], MAX[-round], -MAX constraints = strong MAXV1, MAXWordInitial, NoDiphthong, -il/ils/il y tokens excluded V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -5 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = strong MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded 5.245% MAX[-nasal], MAX[+round], MAX[-round], -contrastive specifications MAXWordInitial, NoDiphthong, -ATR>low V=nucleus, *VV

198

DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -5 most frequent forms only MAX[-back], MAX[+high], MAX[-high], -MAX constraints = strong MAX[+low], MAX[-low], MAX[+nasal], -il/ils/il y tokens excluded 5.32% MAX[-nasal], MAX[+round], MAX[-round], -contrastive specifications MAXWordInitial, NoDiphthong, -low>ATR V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], MAX[-back], MAX[+high], MAX[-high], -5 most frequent forms only MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = strong 4.734% MAX[-nasal], MAX[+round], MAX[-round], -il/ils/il y tokens excluded MAXWordInitial, NoDiphthong, -full specifications V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = strong 16.070% MAX[-nasal], MAX[+round], MAX[-round], -il/ils/il y tokens excluded MAXWordInitial, NoDiphthong, -contrastive specifications V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = strong 15.716% MAX[-nasal], MAX[+round], MAX[-round], -il/ils/il y tokens excluded MAXWordInitial, NoDiphthong, -full specifications V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = strong 15.786% MAX[-nasal], MAX[+round], MAX[-round], -il/ils/il y tokens excluded MAXWordInitial, NoDiphthong, -tokens with [ ௙] excluded V=nucleus, *VV -contrastive specifications DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], -MAX constraints = strong 15.756% MAX[-nasal], MAX[+round], MAX[-round], -il/ils/il y tokens excluded MAXWordInitial, NoDiphthong, -tokens with [ ௙] excluded V=nucleus, *VV -full specifications DEP, MAX[+ATR], MAX[-ATR], MAX[+back], MAX[-back], MAX[+high], MAX[-high], -Singleton underlying forms MAX[+low], MAX[-low], MAX[+nasal], excluded invalid MAX[-nasal], MAX[+round], MAX[-round], -MAX constraints = weak MAXWordInitial, NoDiphthong, -full feature specifications V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], MAX[-back], MAX[+high], MAX[-high], -15 most frequent forms only MAX[+low], MAX[-low], MAX[+nasal], 11.274% -MAX constraints = strong MAX[-nasal], MAX[+round], MAX[-round], MAXWordInitial, V=nucleus, *VV DEP, MAX[+ATR], MAX[-ATR], MAX[+back], -Singleton underlying forms MAX[-back], MAX[+high], MAX[-high], excluded MAX[+low], MAX[-low], MAX[+nasal], invalid -MAX constraints = weak MAX[-nasal], MAX[+round], MAX[-round], -full feature specifications NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-back], MAX[+high], MAX[+low], MAX[+nasal], -Singleton underlying forms MAX[-round], MAXLexical, excluded invalid MAXMonosegmental, MAXV1, -MAX constraints = strong MAXWordInitial, NoDiphthong, V=nucleus, *VV DEP, MAX[+ATR], MAX[-back], MAX[+high], MAX[+low], MAX[+nasal], -Singleton underlying forms MAX[-round], MAXLexical, excluded invalid MAXMonosegmental, MAXV1, -MAX constraints = strong MAXWordInitial, NoDiphthong, -il/ils/il y tokens excluded V=nucleus, *VV

199

DEP, MAX[+ATR], MAX[-back], MAX[+high], MAX[+low], MAX[+nasal], -15 most frequent forms only invalid MAX[-round], MAXWordInitial, -MAX constraints = strong V=nucleus, *VV -Singleton underlying forms DEP, MAX[+back], MAX[+high], excluded MAX[+nasal], MAX[+round], MAXRootNode, -MAX constraints = weak invalid MAXV1, MAXWordInitial, NoDiphthong, -hie rarchy: ATR>high>low> V=nucleus, *VV round>back>nasal -Singleton underlying forms DEP, MAX[+back], MAX[+low], excluded MAX[+nasal], MAX[+round], MAXRootNode, -MAX constraints = weak invalid MAXV1, MAXWordInitial, NoDiphthong, -hierarchy: ATR>high >low> V=nucleus, *VV round>back>nasal DEP, MAX[+back], MAX[+low], -5 most frequent forms only MAXRootNode, MAXV1, MAXWordInitial, -MAX constraints = weak 5.907% NoDiphthong, V=nucleus, *VV -il/ils/il y tokens excluded DEP, MAX[+back], MAX[+low], -5 most frequent forms only MAXRootNode, NoDiphthong, V=nucleus, -MAX constraints = weak invalid *VV -il/ils/il y tokens excluded DEP, MAX[+back], MAX[+nasal], MAX[+round], MAXLexical, -Singleton underlying forms MAXMonosegmental, MAXRootNode, MAXV1, excluded 18.466% MAXWordInitial, NoDiphthong, -MAX constraints = weak V=nucleus, *VV DEP, MAX[+back], MAX[+nasal], -Singleton underlying forms MAX[+round], MAXLexical, MAXRootNode, excluded 17.488% MAXV1, MAXWordInitial, NoDiphthong, -MAX constraints = weak V=nucleus, *VV DEP, MAX[+back], MAX[+nasal], -Singleton underlying forms MAX[+round], MAXMononosegmental, excluded 18.01% MAXRootNode, MAXV1, MAXWordInitial, -MAX constraints = weak NoDiphthong, V=nucleus, *VV -Singleton underlying forms DEP, MAX[+back], MAX[+nasal], excluded MAX[+round], MAXRootNode, MAXV1, -MAX constraints = weak 19.677% MAXWordInitial, NoDiphthong, -hierarchy: low>ATR>high> V=nucleus, *VV back>nasal>round -Singleton underlying forms DEP, MAX[+back], MAX[+nasal], excluded MAX[+round], MAXRootNode, MAXV1, -MAX constraints = weak invalid MAXWordInitial, NoDiphthong, -hierarchy: low>ATR>high> V=nucleus, *VV nasal>back>round -Singleton underlying forms DEP, MAX[+back], MAX[+nasal], excluded MAX[+round], MAXRootNode, MAXV1, -MAX constraints = weak 16.253% MAXWordInitial, NoDiphthong, -hierarchy: low>ATR>high> V=nucleus, *VV nasal>round>back -Singleton underlying forms DEP, MAX[+back], MAX[+nasal], excluded MAX[+round], MAXRootNode, MAXV1, -MAX constraints = weak 16.343% MAXWordInitial, NoDiphthong, -il/ils/il y tokens excluded V=nucleus, *VV -hierarchy: low>ATR>high> round>back>nasal -Singleton underlying forms DEP, MAX[+back], MAX[+nasal], excluded MAX[+round], MAXRootNode, MAXV1, -MAX constraints = weak invalid NoDiphthong, V=nucleus, *VV -hierarchy: ATR>high>low> round>back>nasal -Singleton underlying forms DEP, MAX[+back], MAX[+nasal], excluded MAX[+round], MAXRootNode, -MAX constraints = weak invalid MAXWordInitial, NoDiphthong, -hi erarchy: ATR>high>low> V=nucleus, *VV round>back>nasal

200

-Singleton underlying forms DEP, MAX[+back], MAX[+nasal], excluded MAX[+round], MAXRootNode, -MAX constraints = weak invalid MAXWordInitial, NoDiphthong, -il/ils/il y tokens excluded V=nucleus, *VV -hierarch y: low>ATR>high> round>back>nasal -Singleton underlying forms DEP, MAX[+back], MAX[+nasal], excluded MAX[+round], MAXRootNode, NoDiphthong, -MAX constraints = weak invalid V=nucleus, *VV -hierarchy: ATR>high>low> round>back>nasal -Singleton underlying forms DEP, MAX[+back], MAX[+nasal], excluded MAX[-round], MAXRootNode, MAXV1, -MAX constraints = weak invalid MAXWordInitial, NoDiphthong, -hierarchy: ATR>high>low> V=nucleus, *VV round>back>nasal DEP, MAX[+back], MAX[-back], -Singleton underlying forms MAX[+nasal], MAX[-nasal], MAX[+round], excluded MAX[-round], MAXRootNode, MAXV1, -MAX constraints = weak invalid MAXWordInitial, NoDiphthong, -hierarchy: ATR>high>low> V=nucleus, *VV round>back>nasal DEP, MAX[+back], MAX[-back], -Singleton underlying forms MAX[+nasal], MAX[-nasal], MAX[+round], excluded invalid MAX[-round], MAXWordInitial, -MAX constraints = strong NoDiphthong, V=nucleus, *VV -il/ils/il y tokens excluded DEP, MAX[+back], MAX[-low], -5 most frequent forms only MAXRootNode, MAXWordInitial, -MAX constraints = weak 5.223% NoDiphthong, V=nucleus, *VV -il/ils/il y tokens excluded -Singleton underlying forms DEP, MAX[+round], MAX[-round], excluded MAXWordInitial, NoDiphthong, invalid -MAX constraints = strong V=nucleus, *VV -il/ils/il y tokens excluded DEP, MAXRootNode, MAXV1, -Singleton underlying forms MAXWordInitial, NoDiphthong, 17.44% excluded V=nucleus, *VV MAX[+ATR], MAX[-back], MAX[+high], MAX[+low], MAX[+nasal], MAX[-round], -MAX constraints = strong invalid MAXWordInitial, *VV