Unnatural Phonology: A Synchrony- Diachrony Interface Approach The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:40050094 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Unnatural Phonology: A Synchrony-Diachrony Interface Approach A dissertation presented by Gaˇsper Beguˇs to The Department of Linguistics in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Linguistics Harvard University Cambridge, Massachusetts May 2018 c 2018 Gaˇsper Beguˇs All rights reserved. Dissertation Advisor: Professor Kevin Ryan Author: Gaˇsper Beguˇs Unnatural Phonology: A Synchrony-Diachrony Interface Approach Abstract This dissertation addresses one of the most contested topics in phonology: which factors influence phonological typology and how to disambiguate these factors. I propose a new frame- work for modeling the influences of Analytic Bias and Channel Bias on phonological typology. The focus of the dissertation is unnatural alternations | those that operate in precisely the opposite direction from some universal and articulatorily- or perceptually-motivated phonetic tendency. Based on a typological survey of unnatural alternations and gradient phonotactic restrictions, I propose a diachronic device for explaining unnatural processes called the Blurring Process and argue that minimally three sound changes are required for an unnatural segmental process to arise (Minimal Sound Change Requirement; MSCR). Based on the Blurring Process and MSCR, I propose a new model of deriving typology within Channel Bias. I introduce the concept of Historical Probabilities of Alternations (Pχ) and propose a method of estimating Historical Probabilities based on the statistical technique bootstrapping. The proposed framework has theoretical implications. The existence of unnatural gradient phonotactic restrictions reveals that both categorical Optimality Theory and weighted-constraint frameworks with restricted Con undergenerate. To address this shortcoming, I propose a formal model of phonological typology that combines estimates of Historical Probabilities with results from the artificial grammar learning experiments. The dissertation adopts the Maximum En- tropy model and introduces prior Historical Weights (wχ), which are derived from the Historical Probabilities. Prior variance and Historical Weights allow for a disambiguation between Analytic iii and Channel Bias influences on typology: both metrics are compared to the observed typology, which yields a quantitative comparison between the two factors. To estimate the contribu- tion of Analytic Bias, I conduct an artificial grammar learning experiment that tests learning of a complex and an unnatural alternation. By combining statistical modeling of diachronic developments with experimental work, the proposed framework allows controlling for Channel Bias factors when testing the Analytic Bias influences and vice-versa and, in turn, provides quantitative means for disambiguating Analytic and Channel Bias influences on typology. iv Contents Abstract . iii Abbreviations . xiv Acknowledgements . xv 1 Introduction 1 1.1 Typological discussions . .1 1.2 Outline . .7 2 Unnatural phonology 11 2.1 Background . 11 2.1.1 Subdivision of naturalness . 11 2.1.2 Sound change . 15 2.2 Universal tendencies for voicing . 20 2.2.1 Intervocalic voicing . 21 2.2.2 Post-nasal devoicing . 22 2.2.3 Voicing disagreement . 28 2.2.4 Final voicing . 29 2.3 Post-nasal devoicing . 34 2.3.1 The data . 36 2.3.1.1 Yaghnobi . 36 2.3.1.2 Tswana, Shekgalagari, and Makhuwa . 38 2.3.1.3 Bube and Mpongwe . 40 2.3.1.4 Konyagi . 42 2.3.1.5 South Italian Dialects . 43 2.3.1.6 Buginese and Murik . 44 2.3.1.7 Nasioi . 47 2.3.2 PND as a synchronic alternation . 48 2.4 Unnatural trends in the lexicon . 51 2.4.1 Background . 52 2.4.2 Tarma Quechua . 55 2.4.2.1 Stop voicing . 56 2.4.2.2 Phonetics . 63 2.4.2.3 Productivity . 66 2.4.3 Berawan dialects . 69 2.4.3.1 Restriction against intervocalic voiced stops . 70 v 3 The Blurring Process 77 3.1 Explanations of PND . 78 3.2 A combination of sound changes . 84 3.2.1 Yaghnobi . 85 3.2.2 Tswana, Shekgalagari, and Makhuwa . 90 3.2.3 Bube and Mpongwe . 92 3.2.4 Konyagi . 93 3.2.5 South Italian . 95 3.2.6 Buginese and Murik . 96 3.2.7 Nasioi . 100 3.3 Naturalness of the three sound changes . 101 3.4 The Blurring Process . 107 3.5 Origins of unnatural phonotactics . 120 3.5.1 Previous accounts . 120 3.5.2 A new explanation . 122 3.5.2.1 Berawan . 122 3.5.2.1.1 Diachronic development . 122 3.5.2.1.2 A Blurring Chain . 125 3.5.2.2 Kiput . 131 3.5.2.2.1 Diachronic development . 131 3.5.2.2.2 A Blurring Chain . 135 3.5.2.3 Tarma Quechua . 137 3.5.2.3.1 Diachronic development . 137 3.5.2.3.2 A Blurring Chain . 138 4 Bootstrapping Sound Changes 147 4.1 Historical Probabilities of Alternations . 147 4.2 The model . 153 4.2.1 Sample . 153 4.2.2 Bootstrapping . 155 4.2.2.1 Individual sound changes . 156 4.2.2.2 Two or more sound changes . 157 4.2.2.3 Comparison . 160 4.2.3 Assumptions . 161 4.3 Applications . 164 4.3.1 Estimation of Historical Probabilities . 164 4.3.2 Comparison of alternations . 173 4.3.3 Prediction of attestedness . 176 4.3.4 Comparing Pχ to observed synchronic typology . 179 4.4 Implications . 183 4.4.1 The AB-CB conflation problem . 183 4.4.2 AB-CB complexity mismatch . 185 vi 5 Theoretical implications 191 5.1 Natural Gradience Bias . 192 5.2 A typological model . 199 5.3 Too Many Solutions and future directions . 207 5.3.1 Nasalization as a repair? . 208 5.3.2 Potential solution . 214 6 Learning the Blurring Process 217 6.1 Experiment . 219 6.2 Experimental design . 222 6.2.1 Stimuli . 222 6.2.2 Subjects . 223 6.2.3 Procedure . 223 6.3 Results . 231 6.4 Discussion . 234 6.5 Catalysis . 240 7 Conclusions 245 Appendices 251 A BSC 253 A.1 bsc() . 253 A.2 bsc2() . 255 A.3 summary.bsc() . 258 A.4 summary.bsc2() . 258 A.5 plot.bsc() . 259 A.6 plot.bsc2() . ..