Structure-Dependent Tone Sandhi in Real and Nonce Disyllables in Shanghai Wu

Home , Cantonese phonology

Journal of Phonetics 54 (2016) 169–201

Contents lists available at ScienceDirect

Journal of Phonetics

journal homepage: www.elsevier.com/locate/phonetics

Research Article Structure-dependent tone sandhi in real and nonce disyllables in Shanghai Wu

Jie Zhang n, Yuanliang Meng

Department of Linguistics, The University of Kansas, USA

ARTICLE INFO ABSTRACT

Article history: Disyllabic sequences in Shanghai Wu undergo different types of tone sandhi depending on their structure: Received 20 August 2014 phonological words (e.g., modifier–nouns) spread the initial tone across the disyllable, while phrases (e.g., non- Received in revised form lexicalized verb–nouns) maintain the final tone and level the contour of the nonfinal tone. We investigated the 8 October 2015 productivity of the two tone sandhi types through 48 speakers’ productions of real and nonce disyllables. Our Accepted 13 October 2015 results show that (a) the word-level tone sandhi in Shanghai indeed involves tone spreading, while the phrase- level sandhi is better interpreted as phonetic contour reduction, (b) the spreading sandhi generally applies Keywords: productively to nonce words, but there are some differences in tone production between real and nonce words Tone that are attributable to both categorical non-application and gradient application of the sandhi in nonce words, and Tone sandhi (c) the structure dependency of Shanghai tone sandhi is also productive, as the speakers produced qualitatively Shanghai Wu different f0 patterns in modifier–noun nonce words and verb–noun nonce phrases. These results indicate that in Productivity order to arrive at a full picture of tone sandhi patterning, experimental data that shed light on the generalizations Growth curve analysis that speakers make from the speech input are necessary. & 2015 Elsevier Ltd. All rights reserved.

1. Introduction

1.1. Tone and tone sandhi in Shanghai Wu

Shanghai is a Northern Wu dialect of Chinese spoken in a major metropolis in eastern China with a population of 23.5 million (2010 census data, from http://www.stats-sh.gov.cn/). Like other dialects of Chinese, Shanghai Wu is tonal, but two properties of Shanghai differentiate its tone system from the more familiar four-tone system of Mandarin. First, Shanghai has retained the historical checked syllables (syllables closed by a stop, realized in Shanghai as CVʔ) that Mandarin has lost. These syllables have considerably shorter duration than open or sonorant-closed syllables and a reduced tonal inventory: there are three tones on open or sonorant-closed syllables, transcribed by Xu, Tang, and Qian (1981) in Chao numbers (Chao 1948, 1968) as 53 (T1), 34 (T2), and 13 (T3); but on CVʔ syllables, there are only two phonetic tones 55 (T4) and 12 (T5).1 Second, Shanghai, like many Wu dialects of Chinese, has maintained the historical voicing/phonation distinction in syllable onsets, and the cooccurrence restriction between voicing/phonation and f0, which led to the yin-yang tone split in many Chinese dialects (Karlgren, 1915–1926; Haudricourt, 1954; Pulleyblank, 1978; Yip, 1990, among many others), is still synchronically relevant for Shanghai: the higher tones 53, 34, and 55 (the historical yin tones in Chinese) only occur after voiceless obstruents and modal sonorants and the lower tones 13 and 12 (the historical yang tones) only occur after voiced obstruents and murmured sonorants.2

n Correspondence to: Department of Linguistics, The University of Kansas, 1541 Lilac Lane, Blake Hall, Room 427, Lawrence, KS 66045-3129, USA. Tel.: +1 785 864 2879; fax: +1 785 864 5724. E-mail address: [email protected] (J. Zhang). 1 In Chao numbers, a speaker’s tonal range from low to high is represented by a numerical scale from “1” to “5.” Contour tones are denoted by number concatenations; e.g., “13” indicates a rising tone in the low range (Chao, 1948, 1968). In the tradition of Chinese dialectology, we also use an underline to indicate tones that occur on syllables closed by an obstruent coda, in the case of Shanghai, a glottal stop ʔ. 2 Phonetically, the “voiced” stops in Shanghai are not realized with typical closure voicing, but were described as “voiceless with voiced aspiration” by Chao (1967). More recent studies showed that the voiced category has acoustic properties of breathy phonation such as higher H1–H2 (Cao & Maddieson, 1992; Ren, 1992; Chen, 2011; Gao, Hallé, Honda, Maeda, & Toda, 2011) as well as a shorter closure duration than the voiceless category (Shen & Wang, 1995; Wang, 2011). On fricatives, the voicing distinction is truly reﬂected in voicing. On

Fig. 1. The five phonetic tones in Shanghai. Each tone is exemplified by the average f0 of eight monosyllabic morphemes read in isolation by a female native speaker. The numbers after each tone reflect Xu et al.’s (1981) transcriptions in Chao numbers. Tone 1 (53), Tone 2 (34), and Tone 3 (13) occur on open or sonorant-closed syllables, and Tone 4 (55) and Tone 5 (12) occur on ʔ-closed syllables. Tones 1, 2, and 4 occur after voiceless/modal onsets, and Tones 3 and 5 occur after voiced/murmured onsets.

Fig. 1 illustrates the ﬁve phonetic tones in Shanghai and their cooccurrence with syllable types and onsets. The data came from one female speaker, who read eight monosyllabic morphemes for each tone one time in isolation. The f0 values of the tones were measured using the ProsodyPro script (Xu, 2005–2011) in Praat (Boersma & Weenink, 2009), and the values in Hz were ﬁrst converted into semi-tone, and then z-score transformed. For more details of the stimuli and data analysis, see Section 2. Like in the majority of Chinese dialects, tones in Shanghai participate in tone sandhi depending on the context in which they appear. Comprehensive descriptions of Shanghai tone sandhi in disyllables appeared in Sherard (1972), Zee and Maddieson (1980), Shen (1981), Xu et al. (1981), Xu & Tang (1988), and Zhu (1999, 2006). Two properties of Shanghai tone sandhi are particularly noteworthy. First, the sandhi pattern that occurs in compounds is the so-called “left-dominant sandhi” (Yue-Hashimoto, 1987; Chen, 2000; Zhang, 2007, 2014), which spreads the tone of the initial syllable across the entire word. Examples (1a) and (1b) show that the surface tones of the compounds “to catch a cold” and “popsicle,” 55-31 and 22-44, are derived by spreading the base tones of the initial syllables, 53 and 13, over the disyllables, respectively. This is a notably different pattern from the more familiar third tone sandhi in Mandarin whereby a T3 (213) changes into a T2 (35) before another T3.3 Yue-Hashimoto (1987) and Zhang (2007) termed the Mandarin-type tone sandhi “last-syllable dominant” and “right-dominant,” respectively, and showed from typological data that there is an asymmetry in how the sandhi behaves based on directionality, in that left-dominant sandhi tends to involve the extension of the initial tone rightward, while right-dominant sandhi tends to involve local or paradigmatic tone change. Shanghai and Mandarin, therefore, represent a typical pattern in their respective sandhi directionality.

(1) Shanghai tone sandhi examples: a. sɑ̃53 “to hurt” foŋ53 “wind” /sɑ̃53-foŋ53/-[sɑ̃55-foŋ31] “to catch a cold” (Xu et al., 1981: p. 151) b. bɑ̃13 “stick” pin53 “ice” /bɑ̃13-pin53/-[bɑ̃22-pin44] “popsicle” (Xu et al., 1981: p. 153)

Second, tone sandhi in Shanghai is sensitive to the morphosyntactic structure of the disyllabic sequence. According to Xu et al. (1981) and Xu and Tang (1988), modifier–noun combinations are invariably compounds and can only undergo left-dominant sandhi. Verb–noun, verb-modifier, subject–predicate combinations and coordinate structures that are less lexicalized and have lower frequency of occurrence, however, can undergo right-dominant sandhi, which retains the tone of the final syllable and reduces the tonal contour of the nonfinal syllable. The effects of syntactic structure and frequency of occurrence on Shanghai tone sandhi are illustrated by the examples in (2). In (2a), the same morphemes for “to fry” and “rice”, when concatenated as a modifier–noun compound “fried rice,” undergo left-dominant contour extension, but when concatenated as a verb–noun phrase “to fry rice”, may undergo either left-dominant contour extension or right-dominant contour reduction. In (2b), the verb “to pull” is concatenated with three different nouns –“river”, “grass”, and “tree”, which form an idiomatic expression for “tug-of-war”, a commonly used phrase “to pull out grass; to weed”, and a rarely used phrase “to pull out a tree”, respectively, and the tone sandhi patterns for these three concatenations are left-dominant only, variable left-dominant or right-dominant, and right-dominant only, respectively.

(footnote continued) sonorants, the modal-murmured distinction, which corresponds to the voiceless-voiced distinction in obstruents, is only reported by a subset of the resources, e.g., Xu and Tang (1988) and Zhu (1999), who transcribed the sonorant distinction as ʔCɦC and qCC, respectively. We use this transcription practice here.

3 Acoustically, the third tone sandhi in Mandarin does not involve complete neutralization (Peng, 2000; Yuan & Chen, 2014; among others). But the small acoustic difference between a sandhi T3 and a base T2 cannot be reliably perceived by native speakers (Peng, 2000). J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 171

(2) The effects of syntactic structure and frequency in Shanghai tone sandhi: a. tsʰɔ34 “to fry” vɛ13 “rice” /tsʰɔ34-vɛ13/-[tsʰɔ33-vɛ44] “fried rice” /tsʰɔ34-vɛ13/-[tsʰɔ33-vɛ44] or [tsʰɔ44-vɛ13] “to fry rice” (Xu et al., 1981: p. 149) b. bɑʔ12 “to pull” u13 “river” tsʰɔ34 “grass” zɨ13 “tree” /bɑʔ12-u13/-[bɑʔ11-u13] “tug-of-war” /bɑʔ12-tsʰɔ34/-[bɑʔ11-tsʰɔ13]or[bɑʔ22-tsʰɔ34] “to pull out grass; to weed” /bɑʔ12-zɨ13/-[bɑʔ22-zɨ13] “to pull out a tree” (Xu et al., 1981: p. 148)

The complete patterns of left-dominant and right-dominant sandhis in Shanghai reported in Xu et al. (1981) are summarized in Table 1. Three observations can be made regarding the left-dominant sandhi. First, the tone on the second syllable is entirely determined by the tone on the first syllable and hence completely loses its contrastive status. Second, when the second syllable is open or closed by a nasal, the spreading pattern can be separated into two types depending on the tone on the first syllable: for Tones 1 to 4, the contour on the first syllable is extended across the disyllable, which can be termed contour extension; for Tone 5, however, the contour tone on the first syllable is displaced onto the second syllable, which can be termed contour displacement (see also Zhu, 1999). Third, when the second syllable is CVʔ, only level tones appear on the surface. For right-dominant sandhi, the general pattern is that the first syllable loses the tonal contour while maintaining the overall tone height, and Tones 1 (53) and 2 (34) are neutralized to 44. Xu et al. (1981) argued that the left- vs. right-dominant sandhi directionality is determined by whether the disyllable forms a phonological word, and subsequent phonological analyses of Shanghai tone sandhi and prosodic domains (e.g., Selkirk & Shen, 1990; Duanmu, 1995) have adopted this position, but often assumed that phrases simply do not undergo tone sandhi and right- dominant sandhi only represents phonetic reduction of the nonfinal tones.

1.2. Goals of the current study

A goal of the current study is to provide an acoustic investigation of the two unique properties of Shanghai tone sandhi: rightward tone spreading and structure dependency. Descriptively, we aim to provide acoustic details of both left-dominant and right-dominant tone sandhi in Shanghai in order to (a) verify the spreading property of the left-dominant sandhi reported in earlier literature and (b) shed light on the nature of right-dominant sandhi – is it better interpreted as phonological leveling with prespeciﬁed, neutralized level targets or phonetic contour reduction? In so doing, the study offers a comprehensive acoustic description of disyllabic tone sandhi in a Chinese language with purported bidirectionality, a task hitherto rarely attempted (but see Takahashi, 2013, reviewed below). But more importantly, the study aims to go beyond the sandhi patterns observed in existing words and phrases in Shanghai and test the productivity of both rightward spreading and structure dependency in Shanghai using a nonce-probe test (“wug” test; Berko, 1958). The productivity of a linguistic process refers to its ability to apply to new items (Bybee, 2001: pp.12–13). The understanding of productivity is important to theoretical linguistics as it provides crucial evidence about the generalizations and cognitive abstractions that speakers make and hence directly addresses the issue of grammar in the sense of the tacit knowledge of the speaker (Bybee, 2001, Pierrehumbert, 2003, among many others). In the realm of phonology, productivity is a particularly timely issue as recent experimental research has shown that the speakers’ phonological knowledge as reﬂected in productivity patterns is often not identical to the lexical patterns of the language in question (e.g., Zuraw, 2007; Berent, Steriade, Lennertz, & Vaknin, 2007; Hayes, Zuraw, Siptár, & Londe, 2009; Becker, Ketrez, & Nevins, 2011; Hayes & White, 2013). One factor that has been shown to affect productivity is the phonetic basis of the phonological process. For instance, Zuraw (2007) showed through a corpus study on loans and a web- based survey on nonce words that Tagalog speakers possessed knowledge of the splittability of word-initial consonant clusters that was informed by perception, but could not be deduced from lexical statistics. Hayes et al. (2009) tested Hungarian speakers’

Table 1 Left- and right-dominant sandhis in Shanghai (Xu et al., 1981). “X” indicates a tone in the tonal inventory.

Left-dominant sandhi: Right-dominant sandhi:

σ2 ¼CV or CVN σ2 ¼CVʔ

53-X-55-31 53-X-55-22 53-X-44-X 34-X-33-44 34-X-33-44 34-X-44-X 13-X-22-44 13-X-22-44 13-X-33-X 55-X-33-44 55-X-33-44 55-X-44-X 12-X-11-13 12-X-11-33 12-X-22-X 172 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 knowledge of suffixal vowel harmony through a nonce probe test and showed that although the speakers learned both phonetically natural (suffixed vowels correlated with properties of stem vowels) and unnatural patterns (suffixal vowels correlated with properties of the stem-final consonant), the unnatural patterns were undervalued and learned less robustly than the natural ones. Specific to the rightward spreading tone sandhi in Shanghai, two hypotheses can be made regarding its productivity. First, based on the crosslinguistic prevalence of progressive, assimilatory tonal coarticulation (Mandarin: Xu, 1997; Tianjin: Zhang & Liu, 2011; Taiwanese Southern Min: Peng, 1997; Malaysian Southern Min: Chang & Hsieh, 2012; Vietnamese: Han & Kim, 1974; Brunelle, 2009; Thai: Gandour, Potisuk, & Dechongkit, 1994; Potisuk, Gandour, & Harper, 1997),4 Zhang (2007) argued that left-dominant spreading sandhi is conceivably a phonologized result of it. The strong affinity between rightward spreading sandhi and progressive assimilatory coarticulation predicts that the spreading sandhi should be overall productive, and this hypothesis will be tested by the comparison of the spreading sandhi application between real and nonce words. Second, we expect a productivity difference between contour extension and contour displacement in that the latter would be less productive due to its more distant affinity with progressive coarticulation. Our other goal is to test the hypothesis that the structure sensitivity of the sandhi is productive – a hypothesis rooted in the productivity of morphosyntactic combinations. We test this hypothesis by comparing the tonal realization between two types of disyllabic nonce items – modifier–noun combinations, which form words and are expected to undergo left-dominant sandhi, and verb– noun combinations, which should form phrases due to their nonce nature and hence undergo right-dominant sandhi. If corroborated, this hypothesis will lend direct support to the interface analysis between syntactic structure and prosodic domain in Shanghai (Selkirk & Shen, 1990; Duanmu, 1995). The nonce-probe test used in the comparison may also serve as an additional method that offers empirical evidence for theoretical analyses of prosody–syntax interface in general.

2. Previous literature

2.1. Acoustic studies on Shanghai disyllabic tone sandhi

Despite the relative prestige that Shanghai Wu enjoys as one of the largest dialects of Chinese, there has been relatively little experimental data on the tone sandhi pattern of the dialect. Zee and Maddieson (1980), Toda (1990), Zhu (1999), Chen (2011), and Takahashi (2013) are the precious few exceptions. We restrict ourselves to a review of the disyllabic sandhi pattern – the focus of our study – in these works. Zee and Maddieson (1980) recorded one female speaker and found that the f0 contour of disyllabic compounds was similar in shape to that of the first syllable of the compound. But when the first syllable was a checked syllable with a low rising tone, the rising contour was realized on the second syllable of the disyllable, whose sandhi pattern was analyzed as [L- LM↑], where M↑ indicates a raised Mid. Toda (1990) specifically investigated the tonal realization of disyllables where the first syllable had a high falling tone (T1) as its base tone. In the two speakers that she recorded, both showed a high level tone on the first syllable and a mid falling tone on the second syllable. Toda argued that this pattern was difficult to analyze as a simple contour extension from the base tone of the first syllable due to the difference in the time-normalized f0 contour between the disyllable and the first syllable. Zhu (1999) replicated Toda’s result in one of the two speakers that he recorded, but found that the other speaker’s T1+X pattern could indeed be interpreted as contour extension from the first syllable. Zhu further argued that, while T2+X and T3+X involved contour extension, T4+X and T5+X both involved contour displacement. But the T4+X result was difficult to evaluate due to the small f0 excursion on both the monosyllables and disyllables. Chen’s (2011) primary goal was to investigate how the f0 perturbation from the onset consonant in noninitial position is affected by the phonological consonant-tone cooccurrence restriction, but her results did show that the f0 of the second syllable was primarily determined by the base tone of the first syllable, and the f0 difference associated with the laryngeal feature of the second syllable, which could potentially be linked to a base tone difference, was largely realized in the first 50ms of the vowel and attributable to f0 perturbation. Takahashi (2013) was the only work we are aware of that investigated both left-dominant and right-dominant sandhi in Shanghai, although his left-dominant investigation focused on three- and four-syllable sequences. His data showed that in left-dominant contexts, the f0 contour of the polysyllabic sequence was indeed determined by the base tone of the first syllable, and younger speakers inserted a default Low tone on the third syllable of the sequence. This echoes Chen’s (2008) earlier finding on polysyllabic tone sandhi in Shanghai. For right-dominant sandhi, Takahashi investigated the f0 pattern on the initial syllable of disyllables under different speech rates and found that, at all speech rates, the contour shape of the initial tone was preserved and the falling T1 and rising T2 did not result in neutralization, thus supporting the position that right-dominant sandhi in Shanghai is gradient phonetic reduction rather than neutralizing phonological changes.

4 Regressive tonal coarticulation is commonly attested as well, but its nature may be either assimilatory or dissimilatory. The dissimilatory effect of a Low tone on a preceding High is particularly notable and has been shown in Mandarin (Shih, 1986; Shen, 1990; Xu, 1997), Thai (Gandour et al., 1994; Potisuk et al., 1997), Taiwanese (Peng, 1997), and Yoruba (Laniran, 1992). The duration and magnitude of progressive tonal coarticulation is typically reported to be greater than those of regressive tonal coarticulation, but the opposite effect has occasionally been found (e.g., Mandarin: Shen, 1990; Yoruba: Laniran, 1992; Vietnamese: Brunelle, 2009). In the modeling of prosody, researchers have treated the directionality of coarticulatory smoothing differently. For instance, Kochanski and Shih’s (2003) soft template model (Stem-ML) assumes bidirectional smoothing; Pro-om et al.’s (2009) quantitative target approximation (qTA) model as well as its predecessor, the parallel encoding and target approximation (PENTA) model (Xu, 2005), is sequential and allows only left-to-right coarticulatory inﬂuences. J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 173

2.2. Productivity studies on Chinese tone sandhi

The nonce probe tests, whereby speakers are asked to provide responses to novel words in contexts that are facilitative to the application of the phonological process in question, have been widely used to test the productivity of phonological alternations (e.g., Albright, Andrade, & Hayes, 2001; Hayes & Londe, 2006; Zuraw, 2007; Hayes et al., 2009; Becker et al., 2011; Hayes & White, 2013)as well as regular and irregular morphological rules (e.g., Bybee & Pardo, 1981; Albright, 2002; Albright & Hayes, 2003; Pierrehumbert, 2006). Using this method to investigate the productivity of tone sandhi can be traced back to the work of Hsieh (1970, 1975, 1976) on Taiwanese Southern Min. Subsequent works have investigated the productivity of tone sandhi in Mandarin (Zhang & Lai, 2010), Tianjin (Zhang & Liu, in press), Wuxi (Yan & Zhang, in press), as well as Taiwanese (Wang, 1993; Zhang & Lai, 2008; Zhang, Lai & Sailor, 2011). The major finding is that, similar to the works on segmental phonology cited in Section 1.2, the speakers’ phonological knowledge of tone sandhi is also not necessarily identical to the sandhi patterns reflected in lexical statistics. The works on Taiwanese, for example, have shown that when the tone sandhi involves a circular chain shift, the sandhi is not entirely productive in wug tests, indicating that despite the regularity of the sandhi in the language, the speakers have not completely internalized the pattern and likely rely on lexical and allomorph listings for the sandhi.5 The phonetic property of the sandhi has also been shown to have an influence on how it is internalized by speakers. For instance, Zhang and Lai (2010) tested the productivity of both the third tone sandhi (213-35/__213) and the half-third sandhi (213-21/__T, Ta213) in Mandarin and found that, although both applied categorically to nonce words, the application of the former was phonetically incomplete. They attributed this to the greater phonetic naturalness of the latter.6 Zhang and Liu (in press) replicated this result in the tonal cognates in Tianjin, a dialect closely related to Mandarin. In addition, Yan and Zhang (in press) showed that in Wuxi, a Wu dialect, the productivity of tone sandhi in nonce words is positively correlated with the phonetic similarity between the base tone and the sandhi tone – another effect of the phonetic nature of the sandhi. These previous works indicate that our understanding of tone sandhi can benefit considerably from productivity studies that shed more direct light on the speakers’ tacit knowledge of the sandhi patterns. The results of these productivity studies will then provide a firmer foundation from which formal analyses of tone sandhi can proceed. The productivity studies so far, however, are limited in two respects. First, they have primarily focused on right-dominant sandhi, and we know little about the productivity of the left-dominant spreading pattern common in Northern Wu dialects like Shanghai. This is especially interesting as the rightward spreading pattern is the most closely related to progressive tonal coarticulation. If a strong phonetic basis of the sandhi facilitates its productivity, we would expect the rightward spreading pattern to be relatively productive. Second, previous studies have not investigated the structure sensitivity of tone sandhi. The research on Shanghai that we report here fills these two gaps and complements our current knowledge of tone sandhi productivity.

3. Methodology

The basic methodology of our study was to elicit disyllabic utterances from native speakers of Shanghai by presenting them with two separate monosyllables in their base tones and asking them to pronounce the syllables together as a real word or phrase in Shanghai. The tonal realization of the two syllables was then measured to quantify the application of the tone sandhi. The experiment was divided into two parts, one dealing with existing disyllables, one dealing with nonce disyllables, and both words and phrases, which were expected to undergo left- and right-dominant sandhis, respectively, were tested. We ﬁrst describe our participants and the stimulus construction. The set-up and procedure for the two parts of the experiment are then discussed, followed by how we analyzed the f0 data and the statistical method that we used for f0 curve comparisons.

3.1. Participants

There is considerable dialect-internal variation within Shanghai, and due to close contact with other Wu dialects such as Suzhou and Ningbo as well as the dominant influence of Standard Chinese, Shanghai has undergone and is still undergoing fast changes, especially in its phonetics and lexicon.7 We focused on the variety of Shanghai spoken in the urban area by younger speakers in this study. Our experiment was conducted in the Phonetics Laboratory of the Department of Chinese Language and Literature at Fudan University, Shanghai. Forty-eight speakers (28 females) who grew up in one of the ten urban districts of Shanghai and self-identified as native, fluent speakers of Shanghai Wu participated in the experiment. The majority of the participants were undergraduates at Fudan University, and the participants’ mean age at the time of experiment was 24.6.

5 There is a range of tone sandhi application rates that has been reported for Taiwanese wug tests, and the rate seems to (a) be task-dependent and (b) increase with continued exposure to the nonce items (e.g., Hsieh, 1975; Wang, 1993). Chuang, Chang, and Hsieh (2011) argued that the “foreignness” of the nonce items contributed to the unproductivity results in earlier wug tests and showed that when speakers were asked to undo tone sandhi in existing disyllabic monomorphemic words and Japanese loanwords, the sandhi productivity was considerably higher. They went on to argue that the method of wug tests in the study of productivity needed to be reevaluated. While we agree that the exact application rate of a phonological process in a wug test cannot be directly taken as the productivity of the process, the comparison of wug test results on tone sandhi patterns in different dialects under the same method still informs us that speakers internalize different types of sandhi differently. For instance, Taiwanese tone sandhi induces categorical non-application in nonce words, while Mandarin tone sandhi does not. Moreover, the incorporation of listed lexical items or allomorphs does not preclude the possibility of practice/learning effect as the nonce word becomes more familiar. Zuraw (2000, 2003), for instance, has proposed a model that allows the application rate of semi-productive phonological processes to increase in loanwords as they gradually become incorporated in the lexicon. 6 Zhang & Lai (2010) discussed a number of alternative interpretations for the result, including the low frequency of third tone sandhi cases, treating the low falling tone as the base tone for T3, and the syntactic dependency of the third tone sandhi. Without taking the discussion too far aﬁeld, we refer the reader to their article for a comparison of the interpretations. 7 For information about the diachronic changes, dialectal variation, and sociolinguistic situation of Shanghai, see Xu and Tang (1988), Qian (1997), and Zhu (1999, 2006). 174 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201

3.2. Stimulus construction

3.2.1. Real disyllables The first part of the experiment investigated the nature of left-dominant and right-dominant tone sandhi in real disyllabic sequences in Shanghai. To this end, we aimed to select 100 words and phrases, four for each of the 25 base-tone combinations. Among the four, two should undergo left-dominant sandhi, and two should undergo right-dominant sandhi. We also wanted to ensure that the directionality difference was not simply a function of usage frequency difference, but structure-related. We therefore aimed to match the overall frequency between the left-dominant and right-dominant items. Given that there is no existing frequency corpus of Shanghai, we first designed and implemented an online subjective frequency rating pretest that estimated the usage frequency of 400 disyllabic words and phrases in Shanghai (Balota, Pilotti, & Cortese, 2001). Of the 400 items, 200 undergo left-dominant sandhi and 200 undergo right-dominant sandhi according to Xu et al. (1981) and Xu and Tao (1997). The pronunciation of these 400 items was further checked with two native speaker consultants (one female). Within the 200 in each directionality, each tonal combination was represented by eight items. Our female consultant recorded the entire list and the recording was used for the frequency pretest. The test was divided into four sessions, each with 100 words, and the four sessions were matched in numbers for tones and sandhi types. The test was implemented online in LimeSurvey hosted by the Ermal Garinger Academic Resource Center at the University of Kansas. The test was advertised through Chinese dialect websites, the Linguist List, social media websites, and word of mouth. In the end, the numbers of complete responses for each session were 33, 30, 30, and 33, respectively. Some of our subjects participated in all sessions, some only in a subset of them. During the test, participants were given the Chinese characters and the acoustic recording of an item and asked to respond whether the item was “very commonly used,”“commonly used,”“neither common nor rare,”“rarely used,” or “very rarely used” (given in Chinese characters). The subjects’ ratings were converted into a 1–5 scale, where 1 represents a “very rarely used” response and 5a“very commonly used” response, and the ratings for each item were averaged across subjects. From the 400 items, 100 (50 left- dominant, 50 right-dominant, 4 for each tonal combination) were eventually selected so that the left- and right-dominant items had the same rating distribution (Kolmogorov–Smirnov test: D¼0.14, p¼0.7166) and the same rating mean (Wilcoxon test: W¼1341.5, p¼0.5304; the Wilcoxon test was used due to non-normal distributions of the samples). The syntactic structures of the left-dominant sandhi items were primarily modifier–noun, but also included modifier–verb, coordination, and lexicalized compounds and proper names. The syntactic structures of the right-dominant sandhi items were primarily verb–noun, but also included verb–adverb and subject–predicate. The segmental contents of the syllables were not actively controlled. The complete stimulus list is given in Appendix A. Given that one of the main goals of the study was to investigate the productivity of tone sandhi by comparing the sandhi application in real and nonce words, we elicited the sandhi patterns in real words by providing the speakers with the base tones of individual syllables, as the base tones of the nonce syllables must be given to the subjects (see below). Otherwise, the real words would be read with no auditory priming of the base tones, while the nonce words would. The individual syllables were read in their base tones in isolation by our female consultant and recorded in an anechoic chamber at the University of Kansas. The acoustic files of the individual syllables were then used in the first part of the experiment. To alleviate fatigue of our subjects, who participated in both parts of the experiment, we divided the stimulus list into two, each including one item for left-dominant sandhi and one item for right-dominant sandhi for each of the tonal combinations. One list was used for half of the subjects, and the other list was used for the other half.

3.2.2. Nonce disyllables The second part of the experiment involved the subjects’ production of disyllabic nonce sequences, which were formed by combining a syllable accidentally missing from the Shanghai syllabary (legal segmentals and legal tone, whose combination does not violate voicing-tone cooccurrence restrictions, but happens to be missing in the syllabary) as the first syllable (σ1) and an existing syllable as the second syllable (σ2). The nonce σ1 was provided a meaning as either a nominal modifier or a verb to elicit left- dominant and right-dominant sandhi, respectively, and σ2 was always an existing noun. Ten nonce syllables, two in each tone, were used in σ1 position, and each syllable was associated with two meanings – a modifier meaning and a verb meaning. Each speaker, however, only heard one meaning for each syllable. These nonce syllables were arrived at by first consulting the complete Shanghai syllabary in Zhu (2006, pp. 22–23); the missing segmentals and tone combinations from the syllabary were then checked with both of our consultants for acceptability. Given the voicing-tone cooccurrence restrictions, which limited the number of logical combination, there were relatively few items to choose from, and we were not able to match the segmental properties of these nonce syllables

(e.g., consonant aspiration, vowel height) with those of σ1s in the real disyllables. Ten monosyllabic nouns, two in each tone, were used in σ2 position, and each speaker used one noun to combine with a modifier nonce σ1 and the other to combine with a verb nonce σ1. The two sets of nonce syllables in σ1 and their meanings are given in Table 2, and the two sets of existing nouns used in σ2 are given in Table 3. ~ 53 53 For example, two nonce syllables with Tone 1 (53) were used in σ1 position: ʔmɑ and ʔmu ; half of the speakers would hear ʔmɑ~ 53 used as a modifier meaning “a special color” and ʔmu53 used as a verb meaning “to shop online,” and the other half of the ~ 53 53 speakers would hear ʔmɑ as the verb and ʔmu as the modifier. Each nonce syllable in σ1 was combined with five monosyllabic ~ 53 53 34 13 55 nouns, one in each tone, in σ2. For example, ʔmɑ was combined with sɨ “book”, sɛ “umbrella”, dʑiŋ “musical instrument”, piɪʔ “pen”, diɪʔ12 “flute”, and ʔmu53 was combined with ho53 “flower”, tsʰɔ34 “grass”, zo13 “tea”, tɕyɪʔ55 “chrysanthemum”, ɦmɑʔ12 “sock”. J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 175

Table 2 The two sets of nonce syllables, cued as modiﬁers or verbs, for half of the speakers. For the other half, the meaning columns for the nonce syllables were switched.

T1 ʔmɑ̃53 “a special color” ʔmu53 “to shop online” T2 pʰəŋ34 “a city name” tʰo34 “to sell in a special way” T3 bɤ13 “a man-made nɤ13 “to transport via a material” spaceship” T4 ʔmeʔ55 “a smell” ʔneʔ55 “to smuggle in a special way” T5 ɡueʔ12 “a shape” ʑyɪʔ12 “to give as a gift in a special way”

Table 3 The two sets of nouns that were used to create modiﬁer–noun and verb–noun combinations.

T1 书 sɨ53 “book” 花 ho53 “ﬂower” T2 伞 sɛ34 “umbrella” 草 tsʰɔ34 “grass” T3 琴 dʑiŋ13 “musical instrument” 茶 zo13 “tea” T4 笔 piɪʔ55 “pen” 菊 tɕyɪʔ55 “chrysanthemum” T5 笛 diɪʔ12 “ﬂute” 袜 ɦmɑʔ12 “sock”

Therefore, half of the speakers described “book”, “umbrella”, “musical instrument”, “pen”, and “flute” in a special color whiling shopping online for “flower”, “grass”, “tea”, “chrysanthemum”, and “sock”, while the other half did the opposite, but the segmental contents pronounced by the two groups of speakers were identical. In the end, each speaker produced 25 modifier–noun and 25 verb–noun sequences. The entire stimulus list for this part of the experiment can be found in Appendix B. The cue sentences that provided the meanings for the nonce syllables as well as prompts for the subjects’ response were again recorded by our female consultant.

3.3. Experimental procedure

Each participant first filled out a language background questionnaire and signed an informed consent form, then participated in the experiment. All participants did the real disyllable portion of the experiment first, then the nonce disyllable portion after a five-minute break. They were paid a nominal fee upon the completion of the experiment. Both parts of the experiment were implemented in Paradigms (Tagliaferri, 2010). For the real disyllable portion, the subjects were given the two syllables in their base tones auditorily, separated by an 800 ms pause; the Chinese characters associated with the syllables also appeared on a computer screen as the syllables played. The subjects were then prompted to pronounce the words out loud in a clear and natural way. The stimuli were randomized for each speaker. The main experiment was preceded by an instruction read in Shanghai and a practice session that included four disyllabic items – two left-dominant and two right-dominant – that did not appear in the main experiment. For the nonce disyllable portion, the subjects were given the meanings of the nonce syllables both auditorily and in written form. The nonce syllables were pronounced with their base tones twice during the verbal prompt and represented orthographically with a box “▢” in lieu of a Chinese character on the computer screen. For instance, the subjects would both hear and see “假设上网买物事叫做ʔmɑ~ 53; 如果书还没ʔmɑ~ 53, 那么也可以讲还没___” (“If to shop online is called ʔmɑ~ 53; if a book has not been ʔmɑ~ 53-ed, then we can say that we have not ___”), with the nonce syllable “ʔmɑ~ 53” represented as “▢” on the screen. The subject was expected to reply with /ʔmɑ~ 53书/(“ʔmɑ~ 53-ed the book”) with right-dominant sandhi. For each nonce syllable, the five monosyllabic nouns that it was combined with appeared together in one block; i.e., once the speakers were given the meaning of ʔmɑ~ 53, they were asked to combine it with five different nouns one after another. Different nonce words appeared in random order for each speaker. The main experiment was also preceded by an instruction and a practice session. The practice session used two nonce syllables that were not used in the experiment, one cued as a modifier and one cued as a verb, and the subjects were asked to combine each nonce syllable with two different monosyllabic nouns. The subjects were encouraged to ask questions during and after the practice if they had trouble understanding the task. Some did, but all were judged to have comprehended the task before they moved onto the experiment. For both portions of the experiments, the subjects’ response was continuously recorded using a Marantz solid state recorder PMD 671 sampling at 22.05 kHz and an EV N/D 767a microphone.

3.4. Data analysis

All acoustic analyses of the data were conducted in Praat (Boersma & Weenink, 2009). The rimes of the syllables in the target stimuli were first identified and annotated in a text grid, we then took an f0 measurement at every 10% of the rime duration for each target syllable using ProsodyPro (Xu, 2005–2011), giving eleven f0 measurements for each syllable. ProsodyPro uses the automatic vocal pulse marking by Praat as well as a trimming algorithm that removes spikes and sharp edges (see Appendix 1 of Xu, 1999 for additional information on the trimming algorithm). The Maxf0 and Minf0 parameters in the script as well as the octave-jump cost were 176 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 adjusted for each speaker, and the f0 measurements were hand-checked against narrow-band spectrograms in Praat to correct for octave and other errors in the measurements provided by the script. The f0 measurements in Hz were converted to Semi-tone relative to 50 Hz using the formula in (3a) to better reflect pitch perception (’t Hart, Collier, & Cohen, 1990; Rietveld & Chen, 2006). The Semi-tone values were then z-score transformed using the formula in (3b) over all measurements from a speaker to normalize for between-speaker variations, especially male and female differences (Rose, 1987; Zhu, 2004).

(3) a. ST¼39.87 log10(Hz/50) ST 1∑n ST b. pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffix n i¼1 i zST ¼ x 1 ∑n ð 1∑n Þ2 n1 i¼1 ST i n i¼1 ST i

In addition to the data from the 48 experimental participants, the individual syllables recorded from our language consultant for the first part of the experiment were also analyzed, and these formed the basis for the data on the tonal inventory of Shanghai (see Fig. 1). To compare the rightward spreading sandhi application between real and nonce words and the application of sandhi between modifier–noun words and verb–noun phrases in nonce items, we used growth curve analysis (Mirman, 2014) to model the f0 curves of the two syllables in the subjects’ responses. This analysis describes the functional form of the probability distribution of f0 over time by identifying model fit components for a f0 curve that captures this probability distribution. To capture the changes in f0 direction within a syllable, but in the meantime avoid overfitting the segmental effect, we used quadratic orthogonal polynomials to model all f0 curves over a syllable. The time terms for orthogonal polynomials are uncorrelated, hence their parameter estimates are independent of each other. The intercept term indicates the average height of the curve; the linear term indicates the overall slope of the curve; and the quadratic term indicates the sharpness of the centered peak of the curve. Detailed methods of the f0 comparisons are given together with the results to facilitate the interpretation of the results. In addition, the participants’ tonal response to each target stimulus were also classified by a phonetically trained Shanghai native speaker into “Spreading,”“No Sandhi,” and “Other” to further shed light on the productivity and structure dependency of the sandhi pattern. The speaker was a linguistics graduate student who specialized in tone research and felt comfortable performing the task. She was asked to classify a disyllabic response as “Spreading” if its tone pattern is perceptually equivalent to how she would pronounce an existing nominal compound, “No Sandhi” if she believed that the subject pronounced the disyllable in its base tones, and “Other” if the tone pattern did not fall under either of these two categories. She started from the real disyllables, where she reported the classification to be straightforward, then moved onto the nonce disyllables, where she felt that the classification was difficult for around 20% of the tokens. For these tokens, she used a combination of her perception and a pitch track comparison in Praat between the token in question and a real disyllable in the same syntactic structure by the same speaker to make the final decision. Generalized Linear Mixed-Effects models were then used to investigate the effects of word type (real vs. nonce) and structure on the classification. The rime duration for all stimulus syllables was measured in Praat as well and Linear Mixed-Effects models were used to investigate how duration was affected by word type and structure. All statistical analyses were carried out in R version 3.1.0 (R Core Team, 2014) using the lme4 package version 1.1-6 (Bates, Maechler, Bolker, & Walker, 2014).

4. Results

We first report in Section 4.1 the f0 result on the application of left-dominant and right-dominant sandhis in real disyllables (first part of the experiment). The goal is primarily descriptive: the f0 data shed light on the nature of the two types of sandhi and address the questions of whether the left-dominant sandhi truly involves the spreading of the initial tone rightward, and whether right-dominant sandhi is better interpreted as phonological leveling or phonetic contour reduction. We then report f0 and sandhi classification comparisons between real and nonce words for left-dominant sandhi (Section 4.2) and between modifier–noun words and verb–noun phrases in nonce items (Section 4.3) to address the productivity and structure dependency of the sandhi system. Relevant rime duration comparisons are given in each section as well.

4.1. Acoustic description of left- and right-dominant tone sandhi in real disyllables

The time-normalized f0 data for real disyllabic words expected to undergo left-dominant spreading sandhi, organized by base tone combinations, are given in Fig. 2, and the right-dominant sandhi undergoing counterparts are given in Fig. 3. f0 curves for the base tones from our female language consultant, averaged over the eight monosyllables used for each tone in the real word experiment, were overlaid onto each graph as thin solid lines for reference. All f0 graphs here and elsewhere were produced with the R package ggplot2 (Wickham, 2009). We can compare the two sets of f0 graphs in two ways to understand the nature of the difference between left- and right-dominant sandhi. First, if we look across each row, in which all graphs share the same base tone on the ﬁrst syllable but have different base tones on the second syllable, we can see that in left-dominant sandhi (Fig. 2), the base tone difference on the second syllable is J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 177

Fig. 2. F0 data (vertical lines indicate 7SE) for real disyllabic words expected to undergo left-dominant sandhi. Each graph represents a base-tone combination. Thin solid lines represent the average f0 curves for the base tones from the female language consultant. Each observed data point represents the average f0 at a particular normalized time point across participants. considerably curtailed, and the overall f0 pattern over the disyllable indeed takes the shape of the contour of the first syllable. The f0 on the first syllable is little affected by the different base tones on the second syllable and is generally realized as a slightly falling tone. The spreading pattern is particularly clear in T1+X and T5+X: in the former, the falling contour of the initial base tone was spread over the two-syllable domain, and in the latter, the rising contour of the initial base tone was displaced onto the second syllable, leaving a low tone on the first syllable. For T2+X and T3+X, which had a rising tone as a base tone on the first syllable, only the low portion of the rise was realized on the first syllable, and the f0 was higher on the second syllable in all tonal combinations except for T2+T3, indicating a spreading of the first-syllable rise. In right-dominant sandhi (Fig. 3), however, the f0 on the second syllable remains close to the base tone shape. Like in left-dominant sandhi, the f0 on the first syllable is also little affected by the base tone of the second syllable, but instead of a falling tone, it retains the tonal properties of the base tone. These indicate that no sandhi has applied. Second, if we look down each column, in which all graphs share the same base tone on the second syllable but have different base tones on the first syllable, we can see that in left-dominant sandhi, the f0 on the second syllable is strongly affected by the different base tones on the first syllable and realized differently despite the same base tone. In right-dominant sandhi, however, the f0 on the second syllable within the same column remains constant by maintaining the tonal properties of the base tone. The f0 on the first syllable also maintains properties of the base tone in right-dominant sandhi. In particular, the two tones – T1 (53) and T2 (34) – that have been reported to be neutralized to the same level tone 44 in the literature retained their falling and rising contours on the first syllable, respectively. These comparisons indicate that disyllabic words in Shanghai indeed undergo rightward spreading tone sandhi, but the so-called “right-dominant sandhi” for disyllabic phrases is better interpreted as phonetic contour reduction. We can also note from Fig. 2 that in left-dominant sandhi, the second syllable preserves many of its base tone properties despite the strong influence of the first syllable tone spread. In T2+X, T3+X, and T4+X, the f0 on the second syllable corresponds to the base tones on the second syllable. The trace of the base tones is noticeable for T1+X and T5+X as well: in T1+X, the second syllable has a rise in T1+T2 that corresponds to the rise in base T2, and the second syllable is higher in T1+T4 than T1+T5, corresponding to the base tone difference between T4 and T5; in T5+X, the higher second syllable in T5+T4 than T5+T5 is also 178 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201

Fig. 3. F0 data (vertical lines indicate 7SE) for real disyllabic phrases expected to undergo right-dominant sandhi. Each graph represents a base-tone combination. Thin solid lines represent the f0 curves for the base tones from the female language consultant. Each observed data point represents the average f0 at a particular normalized time point across participants.

clearly observable. The higher f0 on T1, T2, and T4 than T3 and T5 on the second syllable also corresponds to a voicing difference in the onset of the second syllable: along with earlier results (Cao & Maddieson, 1992; Ren, 1992; Chen, 2011; Wang, 2011), our data showed clear voicing for stop closure and frication for obstruents that cooccurred with T3 and T5. These results, therefore, are likely due to a combination of the imitation effect from the exposure to the base tones (see Goldinger, 1998; Delvaux & Soquet, 2007; Tilsen, 2009; Nielsen, 2011, etc. on imitation effects) and the perturbation effect from the initial consonant of the second syllable. The classification result of the f0 patterns into “Spreading,”“No Sandhi,” and “Other” for the real items is given in Fig. 4. The vast majority of forms that are expected to undergo left-dominant sandhi indeed underwent the spreading sandhi, and the vast majority of forms that are expected to undergo right-dominant sandhi underwent no sandhi, as judged by our phonetically trained Shanghai speaker. A Generalized Linear-Mixed Effects model on the “Spreading” pattern, with structure as a fixed effect and participant and item as random effects, showed that the M–N structure had significantly higher “Spreading” counts than the V–N structure (Estimate¼9.6711, S.E.¼0.8259, z¼11.709, p<0.001; M–N as baseline). The rime duration results for the two syllables in the two sandhi directions are given in a box and whiskers plot in Fig. 5. Given that the segmental content between the left- and right-dominant sandhi items was not matched, we coded the vowel height of the rime according to the lowest vocalic element during the rime as “High,”“Mid,” and “Low” (e.g., [tɕʰyø] was coded as “Mid” and [ɕiã] was coded as “Low”), as vowel height is a known factor that affects duration (e.g., House & Fairbanks, 1953; Peterson & Lehiste, 1960; Maddieson, 1997). A likelihood-ratio comparison between a model of rime duration that only included participant and item as random effects and one with vowel height as an additional fixed effect showed that the addition of vowel height significantly improved the model (χ2(2)¼63.646, p<0.001). So this nuisance factor was included in subsequent models, with structure (left- vs. right-dominant) and syllable (σ1 vs. σ2) as potential factors. Likelihood-ratio tests showed that among these models, the one that included both terms and their interaction provided the best fit with the data. From this model, we found that for the left-dominant structure, there was no rime duration difference between σ1 and σ2 (Estimate¼1.432, S.E.¼1.856, t¼0.772, p¼0.440), and for the right-dominant structure, σ2 had a significantly longer rime duration than σ1 (Estimate¼23.945, S.E.¼1.863, t¼12.853, p<0.001). These results indicate that the structural difference is correlated with a difference in duration patterning. J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 179

Fig. 4. Tone pattern counts for “Spreading,”“No Sandhi,” and “Other” for real items as determined by a phonetically trained Shanghai speaker, organized by base tone combinations. “M– N” (modiﬁer–noun) and “V–N” (verb–noun) represent forms that are expected to undergo left-dominant and right-dominant sandhi, respectively.

Fig. 5. Rime durations for the two syllables for the real items in the two sandhi directions. “S1”¼ﬁrst syllable; “s2”¼second syllable. The black dot represents the median, the box represents the interquartile range (1st to 3rd quartile), and the whiskers represent maximally 1.5 times the interquartile range.

4.2. Productivity of the left-dominant sandhi

We focus in this section on the productivity of rightward tone spreading sandhi by comparing the f0 patterns between real and nonce words. The real word data were from the left-dominant-sandhi-undergoing words in the first part of the experiment, while the nonce word data were from the modifier–noun novel compound formations in the second part of the experiment. As stated earlier, the f0 curves were modeled using quadratic orthogonal polynomials. To investigate the effect of word type (real vs. nonce) on the f0 curve for a particular tonal combination, a base model that only included the linear and quadratic time terms and the participant and participant by word type random effects on the time terms was first constructed. Word type was then added onto this model as a factor, and word type’s interactions with the time terms (time, time2) were subsequently added step-wise. Their effects on model fit were evaluated using log-likelihood model comparison. The model fit comparisons for all 50 growth curve analyses (two syllables 25 base-tone combinations) as well as the R codes that generated the models are given in Appendix C. The observed f0 data for these two word types for each of the base-tone combinations together with the second-order orthogonal polynomial growth curve models for each of the syllables are given in Fig. 6. Although model comparisons indicate that the full model is not always justified in all f0 comparisons, it is in some cases. We therefore graphed the full models for all cases to allow for a consistent visual comparison. F0 curves for the base tones from our language consultant were again overlaid onto each graph to aid the assessment of sandhi productivity: a higher similarity between the sandhi tone and the base tone would indicate a lower productivity. 180 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201

Fig. 6. Observed data (symbols, vertical lines indicate7SE) and second-order orthogonal polynomial growth curve model ﬁts (lines) for f0 on disyllabic words expected to undergo left- dominant sandhi. Each graph represents a base-tone combination. Filled circle and thick solid line represent real words; ﬁlled triangle and dotted line represent nonce words; f0 curves for the base tones from our language consultant are overlaid onto each graph as thin solid lines. Each observed data point represents the average f0 at a particular normalized time point across participants.

A visual inspection of the general shapes of the f0 curves in Fig. 6 indicates that the spreading sandhi has generally applied to both real and nonce words: the disyllable has an overall falling contour when the first syllable has a falling tone (T1) and an overall rising contour when the first syllable has a rising tone (T2, T3, T5). This indicates that the spreading sandhi is generally productive, supporting the hypothesis that a close affinity with a phonetic process, here progressive tonal coarticulation, facilitates a sandhi’s productivity. This is also supported by the observation that the f0 curves for the two syllables in both real and nonce words are quite different from those of the base tones overlaid onto the graphs in Fig. 6. For T2+X, however, there was a consistently large yet unexpected difference in average f0 on the first syllable between real and nonce words, and model comparisons showed that the intercept term was significant for all T2+X combinations (χ2(1)>30, p<0.001). An analysis of the experimental stimuli recorded by our consultant indicated that she pronounced the nonce T2 syllables with a lower- than-expected f0, almost in the T3 range, and we believe that this was the cause for the unexpected difference. Despite the general similarity in f0 shape between real and nonce words, model comparisons indicate that the f0 curves from the two types of words are usually significantly different from each other: in 42 out of 50 growth curve analyses, word type has a significant effect on the intercept, linear, or quadratic term of the model; and for all 25 base-tone combinations, the two word types have different f0 curves on at least one of the syllables. There is some evidence that the f0 curves in nonce words show more tonal characteristics of the base tone than those in real words do. This effect would be the most obvious when the expected sandhi tone is a clear falling tone while the base tone is a clear rising tone, or vice versa, a scenario found on the second syllable of T1+T2, T1+T3, and T1+T5 combinations. The corresponding graphs in Fig. 6 show that the nonce words indeed have a greater rising tendency on the second syllable than the real words, and this is supported by model comparisons that showed that the linear terms significantly improved the models (T1+T2: χ2(1)¼5.8341, p¼0.0157; T1+T3: χ2(1)¼30.0598, p<0.001; T1+T5: χ2(1)¼28.5280, p<0.001), and parameter estimates for the linear terms, with real words as the baseline, all showed positive values (T1+T2: 0.5741; T1+T3: 1.3487; T1+T5: 1.3237). J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 181

Fig. 7. Tone pattern counts for “Spreading,”“No Sandhi,” and “Other” real and nonce items expected to undergo left-dominant sandhi as determined by a phonetically trained Shanghai speaker, organized by base tone combinations.

Another scenario where this effect could be observed is when the base tone and sandhi tone are expected to differ in f0 height. This is found when (a) the first syllable has a rising (T3) or high tone (T4) and the second syllable has a low-register tone (T3, T5), which would cause the sandhi tone to be higher than the base tone on the second syllable, or (b) the first syllable has a high falling tone (T1) and the second syllable has a high-register tone (T1, T4), which would cause the base tone to be higher than the sandhi tone on the second syllable. The f0 comparison results, however, are inconsistent. For all tonal combinations in (a), model comparisons showed that the intercept term significantly improved the model (T3+T3: χ2(1)¼6.8648, p¼0.0088; T3+T5: χ2(1)¼ 10.1586, p¼0.0014; T4+T3: χ2(1)¼10.2454, p¼0.0014; T4+T5: χ2(1)¼43.2113, p<0.001), but the parameter estimates for the intercept, with real words as the baseline, only showed negative values for T4+T3 (0.6875) and T4+T5 (0.8529), but a positive values for T3+T3 (0.2037) and T3+T5 (0.3661). For the tonal combinations in (b), the intercept term significantly improved the model for T1+T1 (χ2(1)¼7.7434, p¼0.0054), and the parameter estimate was positive (0.6185), but the intercept term was not significant for T1+T4 (χ2(1)¼0.0176, p¼0.8944). These effects can also be seen in the corresponding graphs in Fig. 6. Another question on productivity we set out to address is whether contour displacement is as productive as contour extension. Contour displacement occurs on T5+X combinations, whereby the rising tone from base T5 is displaced to the second syllable. Given that in the tonal inventory, T1 is the only falling tone, if contour displacement did not apply productively to nonce words, but did apply to real words, we would expect the most marked difference to appear on the second syllable of T5+T1. The corresponding graph in Fig. 6 shows that in real words, the rising tone was indeed displaced onto the second syllable, but in nonce words, the sandhi tone on the second syllable was close to a level tone that was higher than the first syllable. Model comparisons showed that for the f0 on this syllable, adding word type and its interaction with the linear and quadratic time terms stepwise to the base model all significantly improved the previous model (intercept: χ2(1)¼5.6000, p¼0.0180; linear: χ2(1)¼10.5987, p¼0.0011; quadratic: χ2(1)¼ 6.1609, p¼0.0131). The parameter estimate for the linear term is negative and significant (Estimate¼1.5005, t¼4.2857, p<0.001), supporting the claim that the real words had more of a rising contour on this syllable than the nonce words. There are two potential interpretations for this difference. One is that, instead of contour displacement, the more general contour extension has applied to the nonce words. The other is that the level f0 is a result of averaging rising tones from contour displacement and falling tones from the lack of sandhi application. A closer look at the sandhi behaviors from individual tokens showed that the latter interpretation is more accurate. In other words, the nature of the lower productivity for contour displacement is primarily non- application. The sandhi classification result and the f0 result from only the tokens classified as “Spreading” below provide further support for this. The model comparisons for the second syllable of other T5+X combinations, however, showed little effect of word type. Except for the linear term for T5+T2 (χ2(1)¼5.5578, p¼0.0184) and the intercept term for T5+T3 (χ2(1)¼3.9629, p¼0.0465), adding word type or its interactions with time terms did not improve the models. This could mean that contour displacement applied productively to nonce words. But an alternative interpretation is that for T5+T2, T5+T3, and T5+T5, the second syllable had a rising tone as the base tone, and therefore, the application and non-application of contour displacement would both predict a rising tone on the second 182 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201

Table 4 Parameter estimates for the ﬁxed effect of word type (with real words as the baseline) on the “Spreading” pattern counts in the real and nonce items expected to undergo left-dominant sandhi in the Generalized Linear-Mixed Effects models for the 25 base-tone combinations. “nnn”: p<0.001; “nn”: p<0.01; “nn”: p<0.05. For T2+T4, T3+T3, T3+T4, and T4+T4, 100% of the real items exhibited the spreading pattern; to avoid complete separation in the Generalized Linear-Mixed Effects analysis, an artiﬁcial real-item data point that did not undergo spreading was added to the dataset before the analysis was run.

Tones Estimate S.E. z p sig.

T1+T1 1.3451 0.5306 2.5350 0.0112 n T1+T2 9.3582 2.2384 4.1807 <0.001 nnn T1+T3 3.7461 1.1916 3.1438 0.0017 nn T1+T4 3.2494 1.0547 3.0809 0.0021 nn T1+T5 1.6094 0.6080 2.6470 0.0081 nn T2+T1 0.9369 0.4603 2.0353 0.0418 n T2+T2 2.7515 1.0641 2.5857 0.0097 nn T2+T3 0.7388 0.5857 1.2613 0.2072 T2+T4 2.5773 1.2203 2.1119 0.0347 n T2+T5 1.5445 0.7428 2.0793 0.0376 n T3+T1 1.9123 0.8767 2.1812 0.0292 n T3+T2 1.1421 1.1734 0.9733 0.3304 T3+T3 1.1632 1.1732 0.9914 0.3215 T3+T4 1.4733 1.1374 1.2954 0.1952 T3+T5 0.4274 0.9366 0.4564 0.6481 T4+T1 1.5486 0.7962 1.9450 0.0518 T4+T2 3.4720 0.7794 4.4547 <0.001 nnn T4+T3 2.5233 1.0024 2.5172 0.0118 n T4+T4 4.2077 1.0519 4.0000 <0.001 nnn T4+T5 3.4720 0.7794 4.4547 <0.001 nnn T5+T1 3.5850 1.3199 2.7162 0.0066 nn T5+T2 16.0643 6.3021 2.5490 0.0108 n T5+T3 7.1026 3.8792 1.8310 0.0671 T5+T4 15.7873 5.1089 3.0901 0.0020 nn T5+T5 2.3327 0.8968 2.6011 0.0093 nn syllable; for T5+T4, the short duration of the final checked syllable might have prevented the rising contour to surface on the syllable in both real and nonce words. The classification result of the f0 patterns for the nonce words expected to undergo left-dominant sandhi is given in Fig. 7, and the real words’ result is replicated from Fig. 4 here for comparison purposes. A large proportion of the tone patterns in the nonce words has been classified as undergoing the spreading sandhi, indicating the general productivity of the sandhi pattern. But in general, the real words had more “Spreading” patterns and fewer “No Sandhi” patterns than the nonce words. Likelihood ratio comparisons among Generalized Linear-Mixed Effects models on the “Spreading” pattern showed that the inclusion of word type and tonal combination (T1+T2, T1+T3, etc.) both significantly improved upon the model that only included the random effects of participant and item (word type: χ2(1)¼39.393, p<0.001; tonal combination: χ2(24)¼60.721, p<0.001), and the model that includes the interaction term between word type and tonal combination also significantly improved upon the one without the interaction (χ2(24)¼ 75.046, p<0.001). We therefore looked at the effect of word type on the “Spreading” pattern for each tonal combination, and these results are summarized in Table 4. For the 25 base-tone combinations, 18 showed a significant effect of word type. Compared with the f0 curve results in which all 25 tonal combinations showed a significant difference based on word type, these results indicate that the lower sandhi productivity in nonce words suggested by some of the f0 curve results (T1+T1, T1+T2, T1+T3, T1+T5, T4+T3, T4+T5) were caused by a combination of categorical non-application of the sandhi in nonce words and phonetically gradient sandhi application. To further investigate the nature of the productivity difference between real and nonce words, we conducted the same growth curve analyses on only the f0 patterns that have been classified as “Spreading” by our trained Shanghai speaker. The full growth curve models together with the observed f0 data and the base tone data from our consultant are given in Fig. 8, and the model fit comparisons are given in Appendix D. Most of the differences between real and nonce words observed in the entire data set (Fig. 6) persist in Fig. 8. In 41 out of 50 growth curve analyses, word type still has a significant effect on the intercept, linear, or quadratic term of the model; and for all 25 base-tone combinations, the two word types still have different f0 curves on at least one of the syllables. This indicates that the lower productivity indeed partially stems from a gradient application of the sandhi. A particularly interesting comparison appears in the T5+T1 graphs in Fig. 6 and Fig. 8:inFig. 8, where the f0 track only includes those tokens that have been classified as “Spreading,” the f0 on the second syllable is indeed rising, and the inclusion of the interaction between word type and the linear time term did not significantly improved the model (χ2(1)¼0.5685, p¼0.4508). This provides further support for our earlier claim that for T5+T1, the lower productivity of the contour displacement sandhi is primarily reflected in categorical non-application. A concern with the f0 comparison between real and nonce words above is that for each tonal combination, each participant only produced one real word and one nonce word. The results, therefore, are confounded with the segmental perturbation effects on f0 and may not generalize to different items. We investigated the potential effect of vowel height on tones for the tokens classified as “Spreading” as follows. We again coded each syllable according to the lowest vocalic element during the rime as “High,”“Mid,” and “Low”, and for each tone in each syllable position, we investigated the effect of vowel height on f0 by comparing a base model with only time terms and participant and participant by word type random effects on the time terms with a model that includes vowel height J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 183

Fig. 8. Observed data (symbols, vertical lines indicate 7SE) and second-order orthogonal polynomial growth curve model fits (lines) for f0 on disyllabic words that have undergone the left-dominant spreading sandhi according to a phonetically-trained Shanghai speaker. Each graph represents a base-tone combination. Filled circle and thick solid line represent real words; filled triangle and dotted line represent nonce words; f0 curves for the base tones from our language consultant are overlaid onto each graph as thin solid lines. Each observed data point represents the average f0 at a particular normalized time point across participants. using log-likelihood tests. The results consistently showed that the effect of vowel height was significant, and parameter estimates showed that higher vowels generally had a higher f0 than lower vowels (High>Mid>Low), a finding consistent with earlier literature (e.g., Whalen & Levitt, 1995; Maddieson, 1997). To compensate for the vowel height effect, we calculated the average f0 for the high, mid, and low vowels for each tone in each syllable position at the eleven time points and subtracted these values from the original f0 data according to the vowel height of the item. We then reran the growth curve analyses on these values. The results to a large extent replicated the earlier analyses. Thirty-four of the 41 original f0 comparisons that showed a difference between real and nonce words maintained a difference between the two word types, and the vast majority of the effects that were shown to be significant by model comparison (intercept, linear, quadratic) in the original analysis maintained their significance in the new analysis (47 out of 61). This indicates that the word type effects are largely independent from the vowel height effect. The model fit comparisons for the 50 growth curve analyses based on f0 data corrected for vowel height are given in Appendix E. Another potential confound for the f0 comparison between real and nonce words is that the f0 difference may have arisen from a duration difference if the speakers produced the unfamiliar nonce words more slowly. The rime duration results for the two syllables in all of the real and nonce words are given in Fig. 9. We again included vowel height as a nuisance predictor, and model comparisons 2 showed that adding word type (real vs. nonce) or syllable (σ1 vs. σ2) did not significantly improved the model (word type: χ (1)¼ 0.1695, p¼0.6805; syllable: χ2(1)¼1.7184, p¼0.1899), nor did adding the interaction between the two improve the model without the interaction (χ2(1)¼0.4472, p¼0.5037). These results indicate that the duration pattern in the nonce words was identical to that in the real words, and that the f0 difference based on word type was unlikely to be caused by a duration difference.

4.3. Structure dependency

To test the hypothesis that the structure dependency of tone sandhi is productive, we compared the tonal realizations between disyllabic nonce items that have different morphosyntactic structures. We expected the modiﬁer–noun (M–N) combinations to form 184 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201

Fig. 9. Rime durations for the two syllables in real and nonce words expected to undergo left-dominant sandhi. “S1”¼ﬁrst syllable; “s2”¼second syllable. The black dot represents the median, the box represents the interquartile range (1st to 3rd quartile), and the whiskers represent maximally 1.5 times the interquartile range.

Fig. 10. Observed data (symbols, vertical lines indicate 7SE) and second-order orthogonal polynomial growth curve model fits (lines) for f0 on disyllabic nonce items with different syntactic structures (M–N¼modifier–noun; V–N¼verb–noun). Each graph represents a base-tone combination. Filled circle and thick solid line represent M–N words; filled triangle and dotted line represent V–N phrases; f0 curves for the base tones from our language consultant are overlaid onto each graph as thin solid lines. Each observed data point represents the average f0 at a particular normalized time point across participants. words and undergo left-dominant sandhi and the verb–noun (V–N) combinations to form phrases and undergo right-dominant sandhi, which we have interpreted as phonetic contour reduction, not phonological sandhi. The data came from the second part of the experiment. The segmental contents of the M–N and V–N combinations were identical, as the nonce syllables in σ1 position were cued as modifiers for half of the participants and as verbs for the other half. The observed f0 data for the M–N and V–N nonce words for each of the base-tone combinations, along with the second-order orthogonal polynomial growth curve models for the f0 and the f0 curves for the base tones from our language consultant, are given in Fig. 10. We again graphed the full models for all f0 comparisons here for consistency’s sake. J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 185

Fig. 11. Tone pattern counts for “Spreading,”“No Sandhi,” and “Other” for nonce items as determined by a phonetically trained Shanghai speaker, organized by base tone combinations. “M–N” (modiﬁer–noun) and “V–N” (verb–noun) represent forms that are expected to undergo left-dominant and right-dominant sandhi, respectively.

Fig. 12. Rime durations for the two syllables for the nonce items in the two sandhi directions. “S1”¼ﬁrst syllable; “s2”¼second syllable. The black dot represents the median, the box represents the interquartile range (1st to 3rd quartile), and the whiskers represent maximally 1.5 times the interquartile range.

From Fig. 10, we can see that the f0 curves for the V–N nonce items are consistently more similar to the base tones than the M–N nonce items. We have discussed in Section 4.2 that despite some differences from real words, the left-dominant spreading sandhi applied relatively productively in M–N nonce words. For the V–N nonce phrases, however, we generally observed nothing more than the gradient reduction of f0 contours on the first syllable. This is especially clear when the first syllable had a base rising tone (T2+X, T3+X, T5+X). Model comparisons of the f0 curves indicates that in 44 out of the 50 analyses, syntactic structure had a significant effect on the intercept, linear, or quadratic term of the model. When the first syllable has a base rising tone, model comparisons for the first syllable consistently showed that the effect of syntactic structure on the linear term was significant, and parameter estimates, with M–N as the baseline, consistently showed positive values for the linear term, indicating that the first syllables in V–N had greater rising slopes than the first syllables in M–N. Moreover, due to the matched segmental contents between the M–N and V–N combinations, the f0 comparisons here are not confounded with segmental effects, making the result more easily interpretable. The model fit comparisons for all 50 growth curve analyses as well as the R codes are given in Appendix F. The classification result of the f0 patterns for the nonce items with M–N and V–N structures is given in Fig. 11, with the M–N result replicated from Fig. 7. The overwhelming majority of the V–N items has been classified as undergoing no sandhi by our native speaker, and a Generalized Linear-Mixed Effects model on the “Spreading” pattern, with structure as a fixed effect and participant and item as random effects showed that the V–N structure had a significantly fewer “Spreading” count than the M–N structure (Estimate¼5.0304, S.E.¼0.2230, z¼22.559, p<0.001, M–N as baseline). This result is consistent with the f0 curve result and 186 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 suggests that the structure-sensitivity of tone sandhi is productive in Shanghai, and speakers are able to let the syntactic structure of a disyllabic sequence dictate the tonal outcome of the two syllables. The rime duration results for the two syllables in the nonce items with the two structures are given in Fig. 12. We again included vowel height as a nuisance predictor, and likelihood-ratio tests showed that the model that included both the structure (left- vs. right- dominant) and syllable (σ1 vs. σ2) terms as well as their interaction provided the best fit with the data. From this model, we found that for the M–N (left-dominant) structure, the second syllable had a significantly longer duration than the first syllable (Estimate¼3.792, S.E.¼1.919, t¼1.976, p¼0.048), and the for the V–N (right-dominant) structure, the second syllable had a significantly longer duration as well (Estimate¼20.165, SE¼1.919, t¼10.507, p<0.001), but the duration difference between the two syllables was significantly greater for the V–N structure (Estimate¼16.374, S.E.¼2.594, t¼6.311, p<0.001). This durational pattern is similar to that of the real items in Fig. 5 and provides support for the fact that the participants correctly interpreted the grammatical structures for the nonce items.

5. Discussion

5.1. Productivity and structure dependency of Shanghai tone sandhi

Our descriptive results on the f0 patterns of left-dominant and right-dominant tone sandhi in existing disyllabic words provided some evidence that the left-dominant sandhi indeed involved spreading the f0 contour over the two syllables, while the right-dominant sandhi was better interpreted as phonetic contour reduction on the first syllable. The auditory priming of the base tones during the experiment prevented us from making conclusive claims about the nature of these sandhis, but the confound was necessitated by the more important goal of the study, which was to investigate the productivity of the left-dominant sandhi. Our claim that the left- dominant sandhi was relatively productive came from two sets of data, one on the f0 comparison between real and nonce words, one on the comparison between nonce words (M–N structure) and nonce phrases (V–N structure), the former of which would not have been possible had the real word data not been base-tone primed, as the nonce words were necessarily primed by their base tones in the setting up of the context. The real vs. nonce comparison provided a direct test of productivity by showing whether the sandhi applied differently in nonce words than in real words. Despite statistical differences in the f0 curves, we have seen that the shapes of the curves over the disyllabic nonce words generally represent the base tone contours of the first syllable, indicating the productivity of the spreading sandhi. Some of the differences between real and nonce words could be interpreted as the nonce words preserving more tonal characteristics from the base tone than the real words, but not all of them could. The differences, we argue, came from two sources. One was a greater number of categorical non-applications of the sandhi, as shown in the classification result. The other lay in the gradient application of sandhi, a type of gradience that is akin to incomplete neutralization in production (e.g., Peng, 2000; Yu, 2007) and the lack of full productivity in T3 sandhi in Standard Chinese (Zhang & Lai, 2010) and Tianjin (Zhang & Liu, in press). The f0 comparison between M–N nonce words and V–N nonce phrases indicates that the structure sensitivity of the sandhi system in Shanghai is productive, as their differences, both in f0 curves and tone pattern classification, are consistently interpretable by the stronger preservation of the base tones in the V–N structure. The comparison also indirectly supports the productivity of left- dominant sandhi: if the tones in the V–N structure only involved phonetic contour reduction of the base tones, then qualitatively different f0 curves on the M–N nonce words would indicate that phonological sandhi processes have applied to these words. We have found some evidence that the contour displacement sandhi in T5+X has a different productivity pattern from contour extension, as the lower productivity of contour displacement seems to have primarily stemmed from categorical non-application of the sandhi, especially in T5+T1 where the context allows the effect to be the most clearly observed. This suggests a more substantial degree of underlearning of the sandhi. We conjectured earlier that this is due to the sandhi’s more distant affinity with progressive coarticulation than contour extension. Two additional phonetic properties of the sandhi may have also contributed to its lower productivity. One is that according to Zhu (1999), in T5+X combinations, the phonetic prominence falls on the final syllable due to the pronounced rising f0. This creates a mismatch between phonetic prominence and phonological prominence, which is on the initial syllable – the syntactic non-head that determines the sandhi tones (see Selkirk & Shen, 1990; Duanmu, 1995). The other is that pronounced rising tones are typologically disfavored (Zhang, 2002). Other factors identified in earlier literature that undermine tone sandhi productivity include phonological opacity (Hsieh, 1970, 1975, 1976; Wang, 1993; Zhang & Lai, 2008; Zhang et al., 2011), lexical variation (Zhang & Liu, in press), and low lexical frequency (Zhang & Lai, 2008, 2010; Zhang et al., 2011; Zhang & Liu, in press). Opacity and lexical variation are not relevant here, as the contour displacement pattern itself is transparent, and for T5+X combinations, contour displacement is the only sandhi form that has been reported, while the other four contour extension patterns have more reported variation (Xu et al., 1981; Xu & Tang, 1988; Zhu, 1999, 2006). Due to the lack of frequency data, we cannot rule out the possibility that the lower productivity is related to low lexical frequency, but the reported effects of frequency are typically smaller than what we found for T5+T1 combinations (e.g., Zhang & Lai, 2010; Zhang & Liu, in press). An anonymous reviewer raised the concern that the real and nonce items were elicited under different contexts. In particular, the nonce items that varied in the second syllable were elicited in one block, which may have resulted a contrastive reading on the second syllable and consequently more base tone readings (see, for example, Chen & Gussenhoven, 2008). But given that our hypothesis was that the spreading sandhi should be productive in the nonce items, putting these items in a context less conducive to J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 187 tone sandhi stacked the deck against the hypothesis. Therefore, the fact that we found that the sandhi was generally productive in the nonce items provides even stronger support for the hypothesis. Finally, a shortcoming in the design of our experiment is that it did not allow our results to generalize to different items, as each participant only produced one stimulus for each tonal combination. Item, therefore, could not be included as a random factor in our data analysis. Although our f0 comparisons recalibrated against the vowel height effect showed similar patterns, there are many other potential item effects, and we want to emphasize the importance of item generality for future studies.

5.2. Situating Shanghai tone sandhi in tone sandhi typology

Our data on Shanghai tone sandhi complement our knowledge on tone sandhi productivity in the following respects. It is the first of its kind to investigate the productivity of rightward spreading sandhi – a typologically common tone sandhi pattern. Its close affinity to progressive tonal coarticulation prompted the hypothesis that it should be relatively productive, and our results generally support this hypothesis. This result supports the earlier finding that the phonetic naturalness of the tone sandhi facilitates its productivity. A comparison between the Shanghai and Taiwanese results also shows that opacity is a strong cause for categorical unproductivity, as the transparent sandhis in Shanghai showed more gradient production in nonce words, while the opaque sandhis in Taiwanese showed only categorical application, non-application, or misapplication. An additional difference between the Shanghai and Taiwanese patterns not directly reflected in the results is the difficulty with which the tone patterns could be classified. As stated earlier, our Shanghai speaker tasked with classification found the task difficult in around 20% of nonce items; Zhang et al. (2011),on the other hand, did not report similar difficulties and stated that three phonetically trained transcribers agreed on the sandhi transcriptions for virtually all tokens. It is likely that the presence of gradient sandhi application caused the classification difficulty in Shanghai, but the lack of it made the task easier in Taiwanese. Finally, our results, both in f0 and duration, showed that Shanghai speakers made structure-dependent generalizations regarding tone sandhi; the phonological analysis of prosodic domains and prosodic heads in Shanghai and other Chinese dialects (see Duanmu, 1995, 2007, for example), therefore, does have psychological reality despite the fact that phonetic motivations for the analysis are sometimes hard to come by. In general, our results echo Zhang’s (2010, 2014) point regarding Chinese tone sandhi that rushing into an analysis of a sandhi pattern before testing it experimentally is premature, as the speakers’ knowledge of the tone sandhi pattern may not be identical to the pattern in the lexicon, and impressionistic transcriptions, no matter how careful, have their limitations. If we situate our findings here in the recent works in experimental phonology that showed that differences between the speakers’ knowledge and the lexical patterns are informative of the nature of phonological grammar (e.g., Wilson, 2006; Zuraw, 2007; Moreton, 2008; Hayes et al., 2009; Becker et al., 2011), we can more clearly see that the study of Chinese tones has much to gain from experimental investigations of productivity, processing, and learning.

6. Conclusion

Through a production experiment on tone sandhi in both real and nonce disyllables in Shanghai Wu, we found that descriptively, the left-dominant sandhi in phonological words does involve tone spreading, as reported in the literature, while the right-dominant sandhi in phrases is better interpreted as phonetic contour reduction. More importantly, the spreading sandhi in words is largely productive, as indicated by its application to nonce words, but there is some evidence of production differences in the sandhi between real and nonce words that are attributable to both categorical non-application and gradient application of the sandhi in nonce words. The structure dependency of Shanghai tone sandhi is also productive, as shown by the qualitatively different f0 shapes in modifier– noun nonce words and verb–noun nonce phrases. Aside from its contribution to the description of a bidirectional tone sandhi system, this study can also be situated in two theoretical contexts. More generally, it furnishes another example of phonological productivity studies that shed light on the factors that influence phonological learning: within Shanghai, the comparison between contour extension and contour displacement indicates that the affinity with a phonetic pattern might facilitate learning, and comparing the Shanghai results here with those of opaque tone sandhis as in Taiwanese shows that transparency facilitates learning, while opacity hinders it. More specifically, the study provides another window into the inner workings of complex tone sandhi systems. It fills the gap of our knowledge on the productivity of rightward spreading sandhi and structure-dependent sandhi and shows that the grammar of tone sandhi for Shanghai needs to be quantitative and flexible enough to capture both the structure dependency and gradient underlearning of the sandhi. Together with other sandhi productivity studies, it demonstrates the importance of understanding the generalizations that the speakers draw from the learning data and that a proper characterization of the speakers’ knowledge may not lie in the technical difficulties of formalizing the complex mapping between base and sandhi tones, but in capturing the interplay between statistical learning from the lexicon and the often gradient and variable effects of various factors, from formal to functional, that cause underlearning and overlearning.

Acknowledgments

We are grateful to the Department of Chinese Language and Literature at Fudan University for hosting us during our data collection, especially Haifeng Qi, Rujie You, and Dan Yuan. We also thank our Shanghai consultants Zhenzhen Xu and Yifeng Li for helping us with stimuli construction and recording, Melisa Canales at the Ermal Garinger Academic Resources Center at the 188 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201

University of Kansas for programming assistance in the implementation of the online frequency survey, Hanbo Yan for data transcription and analysis, and the experimental participants in Shanghai for taking part in our study. Portions of this work have been presented at the 161st Meeting of the Acoustical Society of America in Seattle, the 86th annual meeting of the Linguistic Society of America in Portland, and the 3rd International Symposium on Tonal Aspects of Languages in Nanjing, China, and we are indebted to the audiences at these venues for their comments and criticisms. For other fruitful discussions on the project, we also thank Rujie You, Joan Sereno, Allard Jongman, Phil Rose, and Richard Wright. Finally, we are grateful to the Editor and the anonymous reviewers from Journal of Phonetics and Chilin Shih, whose comments substantially improved both the content and presentation of this article. This research was supported by the National Science Foundation Grant BCS-0750773. Neither the individuals and institutions cited herein nor the funding agency, however, should be held responsible for the views expressed in this article.

Appendix A. Real disyllables stimulus list

Base Left-dominant sandhi Right-dominant sandhi tones

T1+T1 伤风 sɑ̃foŋ 冰糕 piŋ kɔ 开花 kʰɛ ho 贪多 tʰø tu “to catch a cold”“ice-cream”“to bloom”“to be greedy” T1+T2 香港 ɕiãkɑ̃ 帮衬 pɑ̃tsʰəŋ 烧菜 sɔ tsʰɛ 催款 tsʰø kʰuø “Hong Kong”“help”“to cook dishes”“to dun for payment” T1+T3 当时 tɑ̃zɨ 丝绸 sɨ zɤ 招人 tsɔɦɲiŋ 开晴 kʰɛ ʑiŋ “at that time”“silk”“to hire people”“to become sunny” T1+T4 生吃 sãtɕʰiɪʔ 开脱 kʰɛ tʰəʔ 抽血 tsʰɤ ɕyɪʔ 归国 kuɛ koʔ “to eat raw”“to absolve”“to draw blood”“to return to one’s country” T1+T5 清白 tɕʰiŋ bɑʔ 猪舌 tsɨ zəʔ 扳直 pɛ zəʔ 通敌 tʰoŋ diɪʔ “innocent”“pork tongue”“to straighten”“to collude with the enemy” T2+T1 广州 kuɑ̃tsɤ 假山 kɑ sɛ 打针 tãtsəŋ 套圈 tʰɔ tɕʰyø “Guangzhou”“rockery”“to give an injection”“to toss the rings” T2+T2 喜酒 ɕitɕiɤ 景致 tɕiŋ tsɨ 倒水 tɔ sɨ 做酒 tsu tɕiɤ “wedding feast”“scenary”“to pour water”“to make wine” T2+T3 过房 ku vɑ̃ 顶棚 tiŋ bã 泡茶 pʰɔ zo 救场 tɕiɤ zã “to adopt a young “ceiling”“to steep tea”“to substitute in a performance” relative” T2+T4 舍得 so təʔ 手笔 sɤ piɪʔ 烫脚 tɑ̃tɕiɑʔ 打黑 tãhəʔ “to be willing to part with”“literary skill”“to soak one’s feet”“to combat criminal organizations T2+T5 早熟 tsɔ zoʔ 歹毒 tɛ doʔ 变热 pi ɦɲiɪʔ 打贼 tãzəʔ “precocious”“malicious”“to turn hot”“to hit a thief” T3+T1 豆浆 dɤ tɕiɑ̃ 同乡 doŋ ɕiã 卖书 ɦma sɨ 磨刀 ɦmu tɔ “soy milk”“fellow townsman”“to see books”“to sharpen knives” T3+T2 事体 zɨ tʰi 财富 zɛ fu 领奖 ɦliŋ tɕiã 寻宝 ʑiŋ pɔ “matter”“wealth”“to accept an award”“to seek treasures” T3+T3 寿头 zɤ dɤ 朝廷 zɔ diŋ 汏头 dɑ dɤ 除尘 zɨ zəŋ “fool”“imperial court”“to wash one’s head”“to dust” T3+T4 道德 dɔ təʔ 承德 zəŋ təʔ 汏脚 dɑ tɕiɑʔ 调漆 diɔ tɕʰiɪʔ “morals”“Chengde”“to wash one’s feet”“to mix paint” T3+T5 蛋白 dɛ bɑʔ 住宅 zɨ zɑʔ 乘六 zəŋ loʔ 垫鼻 di biɪʔ “egg white”“house”“to multiply by six”“to get a nose job” T4+T1 八千 pɑʔ tɕʰi 八仙 pɑʔ ɕi 脚酸 tɕiɑʔ sø 摘花 tsəʔ ho “eight thousand”“the eight Taoist “to have sore feet”“to pick ﬂowers” immortals” T4+T2 触气 tsʰoʔ tɕʰi 北海 poʔ hɛ 发嗲 fɑʔ tiɑ 出帐 tsʰəʔ tsã “annoying”“north sea”“to use a girlish voice”“to enter an expenditure in the accounts” T4+T3 竹床 tsoʔ zɑ̃ 国度 koʔ du 吃茶 tɕʰiɪʔ zo 发情 fɑʔ ʑiŋ “bamboo bed”“country”“to drink tea”“to be in heat” T4+T4 角色 koʔ səʔ 漆黑 tɕʰiɑʔ həʔ 发黑 fɑʔ həʔ 脱发 tʰəʔ fɑʔ “role”“pitch-black”“to become black”“to lose hair” T4+T5 积木 tɕiɪʔ ɦmoʔ 北极 poʔ dʑiɪʔ 吸毒 ɕiɪʔ doʔ 出局 tsʰəʔ dʑyɪʔ “building toy”“North Pole”“to use drugs”“to be eliminated” T5+T1 服装 voʔ tsɑ̃ 笛声 diɪʔ sã 属鸡 zoʔ tɕi 夺金 dəʔ tɕiŋ “clothing”“ﬂute sound”“to have the rooster as the zodiac “to win gold” sign” T5+T2 毒品 doʔ pʰiŋ 夺取 dəʔ tɕʰy 拔草 bɑʔ tsʰɔ 服众 voʔ tsoŋ “illegal drugs”“to capture”“to pull out grass”“to convince the public” J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 189

T5+T3 鼻头 biɪʔ dɤ 宅第 zɑʔ di 落地 ɦloʔ di 掘洞 dʑyɪʔ doŋ “nose”“residence”“to land”“to dig a hole” T5+T4 六百 loʔ pɑʔ 薄壁 boʔ piɪʔ 立法 ɦliɪʔ fɑʔ 着色 zəʔ səʔ “six hundred”“thin wall”“to legislate”“to color” T5+T5 墨绿 ɦməʔ loʔ 白族 bɑʔ zoʔ 读碟 doʔ diɪʔ 嚼舌 ʑiɪʔ zəʔ “dark green”“the Bai ethnic group”“to read a disc”“to gossip”

Appendix B. Nonce disyllables stimulus list

The two sets of nouns that are used to create modiﬁer–noun and verb–noun combinations:

1. Left-dominant tone sandhi: The nonce words that the speakers had to produce are in parentheses. The syntaxctic contexts for the nonce words are also provided.

Base Group A Group B tones

T1+X: 假设有一种颜色叫做ʔmɑ~ 53… 假设有一种颜色叫做ʔmu53… “If there is a color called ʔmɑ~ 53…” “If there is a color called ʔmu53…” T1+T1 如果一本书的封面是这种颜色ʔmɑ~ 53,那么也可以讲这是一本（▢书）̥ 如果一朵花是这种颜色ʔmu53,那么也可以讲这是一朵（▢花）。 “If a book’s cover has this color ʔmɑ~ 53, then we can call it a (▢ book).”“If a flower has this color ʔmu53, then we can call it a (▢ flower).” T1+T2 如果一把伞是这种颜色ʔmɑ~ 53,那么也可以讲这是一把（▢伞）。如果一棵草是这种颜色ʔmu53,那么也可以讲这是一棵（▢草）。 “If an umbrella has this color ʔmɑ~ 53, then we can call it a (▢ umbrella).”“If a type of grass has this color ʔmu53, then we can call it (▢ grass).” T1+T3 如果一架琴是这种颜色ʔmɑ~ 53,那么也可以讲这是一架（▢琴）。如果一包茶叶是这种颜色ʔmu53,那么也可以讲这是一包（▢茶）。 “If a musical instrument has this color ʔmɑ~ 53, then we can call it a (▢ “If a pack of tea has this color ʔmu53, then we can call it (▢ tea).” musical instrument).” T1+T4 如果一支笔是这种颜色ʔmɑ~ 53,那么也可以讲这是一支（▢笔）。如果一朵菊花是这种颜色ʔmu53,那么也可以讲这是一朵（▢菊）。 “If a pen has this color ʔmɑ~ 53, then we can call it a (▢ pen).”“If a chrysanthemum has this color ʔmu53, then we can call it a (▢ chrysanthemum).” T1+T5 如果一只笛子是这种颜色ʔmɑ̃53,那么也可以讲这是一只（▢笛）。如果一只袜子是这种颜色ʔmu53,那么也可以讲这是一只（___ 袜）。 “If a flute has this color ʔmɑ̃53, then can call it a (▢ flute).”“If a sock has this color ʔmu53, then we can call it a (▢ sock).” T2+X: 假设有一个城市简称pʰəŋ34… 假设有一个城市简称tʰo34… “If a city’s nickname is pʰəŋ34…” “If a city’s nickname is tʰo34…” T2+T1 如果一本书出版在这个地方pʰəŋ34,那么也可以讲这是一本（▢书）。如果一种花出产在这个地方tʰo34,那么也可以讲这是一种（▢花）。 “If a book was published in this city pʰəŋ34, then we can call it a (▢ “If a type of flowers comes from this city tʰo34, then we can call it (▢ book).” flowers).” T2+T2 如果一把伞出产在这个地方pʰəŋ34,那么也可以讲这是一把（▢伞）。如果一种草出产在这个地方tʰo34,那么也可以讲这是一种（▢草）。 “If an umbrella was manufactured in this city pʰəŋ34, then we can call “If a type of grass comes from this city tʰo34, then we can call it (▢ it a (▢ umbrella).” grass).” T2+T3 如果一架琴出产在这个地方pʰəŋ34,那么也可以讲这是一架（▢琴）。如果一包茶叶出产在这个地方tʰo34,那么也可以讲这是一包（▢ 茶）。 “If a musical instrument was manufactured in this city pʰəŋ34, then we “If a pack of tea was made in this city tʰo34, then we can call it (▢ can call it a (▢ musical instrument).” tea).” T2+T4 如果一支笔出产在这个地方pʰəŋ34,那么也可以讲这是一支（▢笔）。如果一种菊花出产在这个地方tʰo34,那么也可以讲这是一种（▢ 菊）。 “If a pen was manufactured in this city pʰəŋ34, then we can call it a (▢ “If a type of chrysanthemums comes from this city tʰo34, then we pen).” can call it (▢ chrysanthemums).” T2+T5 如果一只笛子出产在这个地方pʰəŋ34,那么也可以讲这是一只（▢ 如果一双袜子出产在这个地方tʰo34,那么也可以讲这是一双（▢ 笛）。袜）。 “If a flute was manufactured in this city pʰəŋ34, then we can call it a (▢ “If a pair of socks comes from this city tʰo34, then we can call it a flute).” pair of (▢ socks).” T3+X: 假设有一种人造材料叫做bɤ13… 假设有一种人造材料叫做nɤ13… 190 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201

“If a man-made material is called bɤ13…” “If a man-made material is called nɤ13…” T3+T1 如果一本书介绍这种材料bɤ13,那么也可以讲这是一本（▢书）。如果一朵假花是这种材料nɤ13,那么也可以讲这是一朵（▢花）。 “If a book is about this material bɤ13, then we can call it a (▢ book).”“If an artificial flower is made of this material nɤ13, then we can call it a (▢ flower).” T3+T2 如果一把伞是这种材料bɤ13,那么也可以讲这是一把（▢伞）。如果一棵假草是这种材料nɤ13,那么也可以讲这是一棵（▢草）。 “If an umbrella is made of this material bɤ13, then we can call it a (▢ “If a piece of artificial grass is made of this material nɤ13, then we umbrella).” can call it a piece of (▢ grass).” T3+T3 如果一架琴是这种材料bɤ13,那么也可以讲这是一架（▢琴）。如果一包假茶叶是这种材料nɤ13,那么也可以讲这是一包（▢茶）。 “If a musical instrument is made of this material bɤ13, then we can call “If a pack of fake tea is made of this material nɤ13, then we can call it a (▢ musical instrument).” it a pack of (▢ tea).” T3+T4 如果一支笔是这种材料bɤ13,那么也可以讲这是一支（▢笔）。如果一朵假菊花是这种材料nɤ13,那么也可以讲这是一朵（▢菊）。 “If a pen is made of this material bɤ13, then we can call it a (▢ pen).”“If an artificial chrysanthemum is made of this material nɤ13, then we can call it a (▢ chrysanthemum).” T3+T5 如果一只笛子是这种材料bɤ13,那么也可以讲这是一只（▢笛）。如果一双袜子是这种材料nɤ13,那么也可以讲这是一双（▢袜）。 “If a flute is made of this material bɤ13, then we can call it a (▢ flute).”“If a pair of socks is made of this material nɤ13, then we can call it a pair of (▢ socks).” T4+X: 假设有一种气味叫做ʔmeʔ55… 假设有一种气味叫做ʔneʔ55… “If there is a smell called ʔmeʔ55…” “If there is a smell called ʔneʔ55…” T4+T1 如果一本书有这种气味ʔmeʔ55,那么也可以讲这是一本（▢书）。如果一朵花有这种气味ʔneʔ55,那么也可以讲这是一朵（▢花）。 “If a book has this smell ʔmeʔ55, then we can call it a (▢ book).”“If a flower has this smell ʔneʔ55, then we can call it a (▢ flower).” T4+T2 如果一把伞有这种气味ʔmeʔ55,那么也可以讲这是一把（▢伞）。如果一棵草有这种气味ʔneʔ55,那么也可以讲这是一棵（▢草）。 “If an umbrella has this smell ʔmeʔ55, then we can call it a (▢ “If a piece of grass has this smell ʔneʔ55, then we can call it a umbrella).” piece of (▢ grass).” T4+T3 如果一架琴有这种气味ʔmeʔ55,那么也可以讲这是一架（▢琴）。如果一包茶叶有这种气味ʔneʔ55,那么也可以讲这是一包（▢茶）。 “If a musical instrument has this smell ʔmeʔ55, then we can call it a (▢ “If a pack of tea has this smell ʔneʔ55, then we can call it a pack of musical instrument).” (▢ tea).” T4+T4 如果一支笔有这种气味ʔmeʔ55,那么也可以讲这是一支（▢笔）。如果一朵菊花有这种气味ʔneʔ55,那么也可以讲这是一朵（▢菊）。 “If a pen has this smell ʔmeʔ55, then we can call it a (▢ pen).”“If a chrysanthemum has this smell ʔneʔ55, then we can call it a (▢ chrysanthemum).” T4+T5 如果一只笛子有这种气味ʔmeʔ55,那么也可以讲这是一只（▢笛）。如果一双袜子有这种气味ʔneʔ55,那么也可以讲这是一双（▢袜）。 “If a flute has this smell ʔmeʔ55, then we can call it a (▢ flute).”“If a pair of socks has this smell ʔneʔ55, then we can call it a pair of (▢ socks).” T5+X: 假设有一种形状叫做ɡueʔ12… 假设有一种形状叫做ʑyɪʔ12… “If there is a shape called ɡueʔ12…” “If there is a shape called ʑyɪʔ12…” T5+T1 如果一本书是这种形状ɡueʔ12,那么也可以讲这是一本（▢书）。如果一朵花是这种形状ʑyɪʔ12,那么也可以讲这是一朵（▢花）。 “If a book has this shape ɡueʔ12, then we can call it a (▢ book).”“If a flower has this shape ʑyɪʔ12, then we can call it a (▢ flower).” T5+T2 如果一把伞是这种形状ɡueʔ12,那么也可以讲这是一把（▢伞）。如果一棵草是这种形状ʑyɪʔ12,那么也可以讲这是一棵（▢草）。 “If an umbrella has this shape ɡueʔ12, then we can call it a (▢ “If a piece of grass has this shape ʑyɪʔ12, then we can call it a umbrella).” piece of (▢ grass).” T5+T3 如果一架琴是这种形状ɡueʔ12,那么也可以讲这是一架（▢琴）。如果一片茶叶是这种形状ʑyɪʔ12,那么也可以讲这是一片（▢茶）。 “If a musical instrument has this shape ɡueʔ12, then we can call it a (▢ “If a tea leaf has this shape ʑyɪʔ12, then we can call it a leaf of (▢ musical instrument).” tea).” T5+T4 如果一支笔是这种形状ɡueʔ12,那么也可以讲这是一支（▢笔）。如果一朵菊花是这种形状ʑyɪʔ12,那么也可以讲这是一朵（▢菊）。 “If a pen has this shape ɡueʔ12, then we can call it a (▢ pen).”“If a chrysanthemum has this shape ʑyɪʔ12, then we can call it a (▢ chrysanthemum).” T5+T5 如果一只笛子是这种形状ɡueʔ12,那么也可以讲这是一只（▢笛）̥ 如果一只袜子是这种形状ʑyɪʔ12,那么也可以讲这是一只（▢袜）̥ “If a flute has this shape ɡueʔ12, then we can call it a (▢ flute).”“If a sock has this shape ʑyɪʔ12, then we can call it a (▢ sock).”

2. Right-dominant tone sandhi: The nonce words that the speakers had to produce are in parentheses. The syntactic contexts for the nonce words are also provided.

Base Group A Group B tones

T1+X: 假设上网买物事叫做ʔmu53… 假设上网买物事叫做ʔmɑ~ 53… “If to shop online is called ʔmu53…” “If to shop online is called ʔmɑ~ 53…” T1+T1 如果花还没ʔmu53, 那么也可以讲还没（▢花）。如果书还没ʔmɑ~ 53, 那么也可以讲还没（▢书）。 “If ﬂowers have not been ʔmu53-ed, then we can say that we have “If the book has not been ʔmɑ~ 53-ed, then we can say that we have not not (▢ ﬂowers).” (▢ book).” T1+T2 如果草还没ʔmu53, 那么也可以讲还没（▢草）。如果伞还没ʔmɑ~ 53, 那么也可以讲还没（▢伞）。 “If grass has not been ʔmu53-ed, then we can say that we have not “If the umbrella has not been ʔmɑ~ 53-ed, then we can say that we have (▢ grass).” not (▢ umbrella).” T1+T3 如果茶还没ʔmu53, 那么也可以讲还没（▢茶）。如果琴还没ʔmɑ~ 53, 那么也可以讲还没（▢琴）。 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 191

“If tea has not been ʔmu53-ed, then we can say that we have not “If the musical instrument has not been ʔmɑ~ 53-ed, then we can say (▢ tea).” that we have not (▢ musical instrument).” T1+T4 如果菊花还没ʔmu53, 那么也可以讲还没（▢菊）。如果笔还没ʔmɑ~ 53, 那么也可以讲还没（▢笔）。 “If chrysanthemums have not been ʔmu53-ed, then we can say “If the pen has not been ʔmɑ~ 53-ed, then we can say that we have not that we have not (▢ chrysanthemums).” (▢ pen).” T1+T5 如果袜子还没ʔmu53, 那么也可以讲还没（___袜）。如果笛子还没ʔmɑ~ 53, 那么也可以讲还没（___笛）。 “If socks have not been ʔmu53-ed, then we can say that we have “If the flute has not been ʔmɑ~ 53-ed, then we can say that we have not not (▢ socks).” (▢ flute).” T2+X: 假设有一种销售方式叫做tʰo34… 假设有一种销售方式叫做pʰəŋ34… “If a form of sales is called tʰo34…” “If a form of sales is called pʰəŋ34…” T2+T1 如果花还没tʰo34,那么也可以讲还没（▢花）。如果书还没pʰəŋ34,那么也可以讲还没（▢书）。 “If flowers have not been tʰo34-ed, then we can say that we have “If the book has not been pʰəŋ34-ed, then we can say we have not (▢ not (▢ flowers).” book).” T2+T2 如果草还没tʰo34,那么也可以讲还没（▢草）。如果伞还没pʰəŋ34,那么也可以讲还没（▢伞）。 “If grass has not been tʰo34-ed, then we can say that we have not “If the umbrella has not been pʰəŋ34-ed, then we can say we have not (▢ grass).” (▢ umbrella).” T2+T3 如果茶还没tʰo34,那么也可以讲还没（▢茶）。如果琴还没pʰəŋ34,那么也可以讲还没（▢琴）。 “If tea has not been tʰo34-ed, then we can say that we have not (▢ “If the musical instrument has not been pʰəŋ34-ed, then we can say we tea).” have not (▢ musical instrument).” T2+T4 如果菊花还没tʰo34,那么也可以讲还没（▢菊）。如果笔还没pʰəŋ34,那么也可以讲还没（▢笔）。 “If chrysanthemums have not been tʰo34-ed, then we can say that “If the pen has not been pʰəŋ34-ed, then we can say we have not (▢ we have not (▢ chrysanthemums).” pen).” T2+T5 如果袜子还没tʰo34,那么也可以讲还没（▢袜）。如果笛子还没pʰəŋ34,那么也可以讲还没（▢笛）。 “If socks have not been tʰo34-ed, then we can say that we have not “If the flute has not been pʰəŋ34-ed, then we can say we have not (▢ (▢ socks).” flute).” T3+X: 假设用飞船运输叫做nɤ13… 假设用飞船运输叫做bɤ13… “If to transport via a spaceship is called nɤ13…” “If to transport via a spaceship is called bɤ13…” T3+T1 如果花还没nɤ13,那么也可以讲还没（▢花）。如果书还没bɤ13,那么也可以讲还没（▢书）。 “If flowers have not been nɤ13-ed, then we can say we have not (▢ “If the book has not been bɤ13-ed, then we can say we have not (▢ flowers).” book).” T3+T2 如果草还没nɤ13,那么也可以讲还没（▢草）。如果伞还没bɤ13,那么也可以讲还没（▢伞）。 “If grass has not been nɤ13-ed, then we can say we have not (▢ “If the umbrella has not been bɤ13-ed, then we can say we have not (▢ grass).” umbrella).” T3+T3 如果茶还没nɤ13,那么也可以讲还没（▢茶）。如果琴还没bɤ13,那么也可以讲还没（▢琴）。 “If tea has not been nɤ13-ed, then we can say we have not (▢ tea).”“If the musical instrument has not been bɤ13-ed, then we can say we have not (▢ musical instrument).” T3+T4 如果菊花还没nɤ13,那么也可以讲还没（▢菊）。如果笔还没bɤ13,那么也可以讲还没（▢笔）。 “If chrysanthemums have not been nɤ13-ed, then we can say we “If the pen has not been bɤ13-ed, then we can say we have not (▢ have not (▢ chrysanthemums).” pen).” T3+T5 如果袜子还没nɤ13,那么也可以讲还没（▢袜）。如果笛子还没bɤ13,那么也可以讲还没（▢笛）。 “If socks have not been nɤ13-ed, then we can say we have not (▢ “If the flute has not been bɤ13-ed, then we can say we have not (▢ socks).” flute).” T4+X: 假设有一种走私方式叫做ʔneʔ55… 假设有一种走私方式叫做ʔmeʔ55… “If a form of smuggling is called ʔneʔ55…” “If a form of smuggling is called ʔmeʔ55…” T4+T1 如果花还没ʔneʔ55,那么也可以讲还没（▢花）。如果书还没ʔmeʔ55,那么也可以讲还没（▢书）。 “If flowers have not been ʔneʔ55-ed, then we can say that we have “If the book has not been ʔmeʔ55-ed, then we can say that we have not (▢ flowers).” not (▢ book).” T4+T2 如果草还没ʔneʔ55,那么也可以讲还没（▢草）。如果伞还没ʔmeʔ55,那么也可以讲还没（▢伞）。 “If grass has not been ʔneʔ55-ed, then we can say that we have “If the umbrella has not been ʔmeʔ55-ed, then we can say that we not (▢ grass).” have not (▢ umbrella).” T4+T3 如果茶还没ʔneʔ55,那么也可以讲还没（▢茶）。如果琴还没ʔmeʔ55,那么也可以讲还没（▢琴）。 “If tea has not been ʔneʔ55-ed, then we can say that we have not “If the musical instrument has not been ʔmeʔ55-ed, then we can say (▢ tea).” that we have not (▢ musical instrument).” T4+T4 如果菊花还没ʔneʔ55,那么也可以讲还没（▢菊）。如果笔还没ʔmeʔ55,那么也可以讲还没（▢笔）。 “If chrysanthemums have not been ʔneʔ55-ed, then we can say “If the pen has not been ʔmeʔ55-ed, then we can say that we have not that we have not (▢ chrysanthemums).” (▢ pen).” T4+T5 如果袜子还没ʔneʔ55,那么也可以讲还没（▢袜）。如果笛子还没ʔmeʔ55,那么也可以讲还没（▢笛）。 “If socks have not been ʔneʔ55-ed, then we can say that we have “If the flute has not been ʔmeʔ55-ed, then we can say that we have not not (▢ socks).” (▢ flute).” T5+X: 假设有一种赠送方式叫做ʑyɪʔ12… 假设有一种赠送方式叫做ɡueʔ12… “If there is a form of gift-giving called ʑyɪʔ12…” “If there is a form of gift-giving called ɡueʔ12…” T5+T1 如果花还没ʑyɪʔ12,那么也可以讲还没（▢花）。如果书还没ɡueʔ12,那么也可以讲还没（▢书）。 “If flowers have not been ʑyɪʔ12-ed, then we can say that we have “If the book has not been ɡueʔ12-ed, then we can say that we have not not (▢ flowers).” (▢ book).” T5+T2 如果草还没ʑyɪʔ12,那么也可以讲还没（▢草）。如果伞还没ɡueʔ12,那么也可以讲还没（▢伞）。 192 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201

“If grass has not been ʑyɪʔ12-ed, then we can say that we have not “If the umbrella has not been ɡueʔ12-ed, then we can say that we have (▢ grass).” not (▢ umbrella).” T5+T3 如果茶还没ʑyɪʔ12,那么也可以讲还没（▢茶）。如果琴还没ɡueʔ12,那么也可以讲还没（▢琴）。 “If tea has not been ʑyɪʔ12-ed, then we can say that we have not “If the musical instrument has not been ɡueʔ12-ed, then we can say (▢ tea).” that we have not (▢ musical instrument).” T5+T4 如果菊花还没ʑyɪʔ12,那么也可以讲还没（▢菊）。如果笔还没ɡueʔ12,那么也可以讲还没（▢笔）。 “If chrysanthemums have not been ʑyɪʔ12-ed, then we can say “If the pen has not been ɡueʔ12-ed, then we can say that we have not that we have not (▢ chrysanthemums).” (▢ pen).” T5+T5 如果袜子还没ʑyɪʔ12,那么也可以讲还没（▢袜）。如果笛子还没ɡueʔ12,那么也可以讲还没（▢笛）。 “If socks have not been ʑyɪʔ12-ed, then we can say that we have “If the ﬂute has not been ɡueʔ12-ed, then we can say that we have not not (▢ socks).” (▢ ﬂute).”

Appendix C. Model ﬁt comparisons for f0 curves between real and nonce disyllabic words expected to undergo left- dominant sandhi

The dependent variable for the models is f0inz-score-transformed semi-tone values. The base model includes the linear and quadratic time terms and the participant and participant by word type (real vs. nonce) random effects on the time terms. The intercept, linear, and quadratic terms represent the effects of the successive addition of condition (with real words as the baseline) and its interaction with time and time2 and indicate whether the two curves have different average f0, slope, and sharpness of the centered peak. Terms that signiﬁcantly improve the previous model at the p<0.05 model are indicated in bold. As the analyses were restricted to two conditions, the parameter estimates for each condition are not reported, as the change in deviance directly reﬂects the difference between the two conditions. R codes for the models: data.subset.base < lmer(F0 (ot1+ot2)+(ot1+ot2|Speaker)+(ot1+ot2|Speaker:Wordtype), control¼lmerControl(optimizer¼"bobyqa"), data.subset, REML¼F) data.subset.0 < lmer(F0 (ot1+ot2)+Wordtype+(ot1+ot2|Speaker)+(ot1+ot2|Speaker:Wordtype), control¼lmerControl(optimizer¼"bobyqa"), data.subset, REML¼F) data.subset.1 <- lmer(F0 (ot1+ot2)+Wordtype+ot1:Wordtype+(ot1+ot2|Speaker)+(ot1+ot2| Speaker:Wordtype), control¼lmerControl(optimizer¼"bobyqa"), data.subset, REML¼F) data.subset.2 <- lmer(F0 (ot1+ot2)+(ot1+ot2)nWordtype+(ot1+ot2|Speaker)+(ot1+ot2|Speaker: Wordtype), control¼lmerControl(optimizer¼"bobyqa"), data.subset, REML¼F)

Base tones σ1 σ2

LogLik Deviance χ2(1) p LogLik Deviance χ2(1) p

T1+T1 Base 166.7646 333.5292 ––Base 109.6861 219.3722 –– Intercept 169.7207 339.4415 5.9123 0.0150 Intercept 105.8144 211.6288 7.7434 0.0054 Linear 171.5499 343.0998 3.6583 0.0558 Linear 103.6278 207.2555 4.3733 0.0365 Quadratic 172.5958 345.1917 2.0919 0.1481 Quadratic 101.3415 202.6829 4.5726 0.0325 T1+T2 Base 229.2847 458.5694 ––Base 88.6473 177.2945 –– Intercept 233.3053 466.6105 8.0411 0.0046 Intercept 88.5753 177.1506 0.1440 0.7044 Linear 239.8058 479.6115 13.0010 0.0003 Linear 85.6582 171.3164 5.8341 0.0157 Quadratic 240.2651 480.5301 0.9186 0.3378 Quadratic 85.5306 171.0612 0.2552 0.6134 T1+T3 Base 334.4540 668.9080 ––Base 122.4881 244.9763 –– Intercept 338.4739 676.9478 8.0398 0.0046 Intercept 122.0554 244.1109 0.8654 0.3522 Linear 341.9591 683.9182 6.9703 0.0083 Linear 107.0255 214.0511 30.0598 0.0000 Quadratic 342.3595 684.7189 0.8007 0.3709 Quadratic 106.5382 213.0764 0.9747 0.3235 T1+T4 Base 300.4756 600.9513 ––Base 51.7230 103.4460 –– Intercept 306.1068 612.2136 11.2623 0.0008 Intercept 51.7142 103.4284 0.0176 0.8944 Linear 308.0757 616.1513 3.9377 0.0472 Linear 50.5032 101.0064 2.4220 0.1196 Quadratic 312.0109 624.0219 7.8705 0.0050 Quadratic 49.8348 99.6696 1.3367 0.2476 T1+T5 Base 349.9094 699.8188 ––Base 5.5089 11.0178 –– Intercept 353.2737 706.5475 6.7287 0.0095 Intercept 5.5449 11.0899 0.0720 0.7884 Linear 353.2766 706.5532 0.0057 0.9399 Linear 19.8089 39.6178 28.5280 0.0000 Quadratic 358.4090 716.8181 10.2649 0.0014 Quadratic 20.8826 41.7651 2.1473 0.1428 T2+T1 Base 42.5486 85.0973 ––Base 244.5536 489.1073 –– Intercept 57.8218 115.6437 30.5464 0.0000 Intercept 244.7123 489.4245 0.0000 1.0000 Linear 61.0096 122.0193 6.3756 0.0116 Linear 244.4129 488.8257 0.5988 0.4390 Quadratic 64.0705 128.1410 6.1217 0.0134 Quadratic 244.2824 488.5647 0.2610 0.6094 T2+T2 Base 222.2871 444.5741 ––Base 202.8412 405.6824 –– J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 193

Intercept 246.5563 493.1126 48.5385 0.0000 Intercept 196.1282 392.2565 13.4259 0.0002 Linear 246.6320 493.2640 0.1514 0.6972 Linear 195.6151 391.2303 1.0262 0.3110 Quadratic 251.7207 503.4414 10.1774 0.0014 Quadratic 192.9060 385.8120 5.4183 0.0199 T2+T3 Base 237.3931 474.7863 ––Base 253.4799 506.9598 –– Intercept 259.8810 519.7619 44.9756 0.0000 Intercept 253.4612 506.9225 0.0373 0.8468 Linear 260.1384 520.2768 0.5148 0.4731 Linear 246.5524 493.1048 13.8176 0.0002 Quadratic 265.7460 531.4920 11.2152 0.0008 Quadratic 239.4464 478.8929 14.2119 0.0002 T2+T4 Base 257.6640 515.3279 ––Base 27.0470 54.0941 –– Intercept 285.1142 570.2284 54.9005 0.0000 Intercept 32.0990 64.1981 10.1040 0.0015 Linear 285.3301 570.6601 0.4317 0.5111 Linear 39.1611 78.3221 14.1240 0.0002 Quadratic 285.3316 570.6632 0.0030 0.9562 Quadratic 40.2266 80.4533 2.1312 0.1443 T2+T5 Base 292.0008 584.0016 ––Base 360.5952 721.1904 –– Intercept 308.1851 616.3701 32.3685 0.0000 Intercept 363.0957 726.1913 5.0009 0.0253 Linear 308.1861 616.3722 0.0021 0.9634 Linear 366.6506 733.3012 7.1099 0.0077 Quadratic 311.3176 622.6352 6.2630 0.0123 Quadratic 367.8177 735.6355 2.3343 0.1266 T3+T1 Base 59.0694 118.1387 ––Base 120.8641 241.7283 –– Intercept 57.9199 115.8397 2.2990 0.1295 Intercept 120.2367 240.4734 1.2548 0.2626 Linear 54.2476 108.4952 7.3445 0.0067 Linear 116.2591 232.5182 7.9552 0.0048 Quadratic 50.3388 100.6776 7.8176 0.0052 Quadratic 113.7970 227.5940 4.9242 0.0265 T3+T2 Base 45.9671 91.9342 ––Base 42.2469 84.4939 –– Intercept 46.4789 92.9578 1.0236 0.3117 Intercept 35.1162 70.2323 14.2615 0.0002 Linear 48.5778 97.1556 4.1978 0.0405 Linear 35.1119 70.2239 0.0084 0.9268 Quadratic 48.9314 97.8629 0.7072 0.4004 Quadratic 33.7951 67.5901 2.6338 0.1046 T3+T3 Base 235.1262 470.2524 ––Base 26.1851 52.3702 –– Intercept 235.7316 471.4632 1.2108 0.2712 Intercept 29.6175 59.2350 6.8648 0.0088 Linear 237.0340 474.0679 2.6047 0.1065 Linear 29.9001 59.8002 0.5652 0.4522 Quadratic 240.4742 480.9483 6.8804 0.0087 Quadratic 29.9070 59.8140 0.0137 0.9067 T3+T4 Base 52.5259 105.0519 ––Base 75.7638 151.5275 –– Intercept 52.4646 104.9291 0.1228 0.7261 Intercept 73.2187 146.4374 5.0902 0.0241 Linear 52.3978 104.7955 0.1336 0.7147 Linear 73.1631 146.3262 0.1111 0.7389 Quadratic 45.8941 91.7883 13.0073 0.0003 Quadratic 73.1459 146.2918 0.0344 0.8528 T3+T5 Base 447.8351 895.6701 ––Base 259.6213 519.2426 –– Intercept 447.8634 895.7267 0.0566 0.8119 Intercept 264.7006 529.4012 10.1586 0.0014 Linear 447.8635 895.7270 0.0003 0.9858 Linear 264.9833 529.9667 0.5655 0.4521 Quadratic 447.8771 895.7541 0.0271 0.8692 Quadratic 264.9531 529.9061 0.0000 1.0000 T4+T1 Base 430.8080 861.6160 ––Base 222.7261 445.4521 –– Intercept 431.0958 862.1916 0.5756 0.4480 Intercept 223.0835 446.1670 0.0000 1.0000 Linear 433.3028 866.6055 4.4139 0.0356 Linear 222.2600 444.5200 1.6470 0.1994 Quadratic 434.2462 868.4924 1.8868 0.1696 Quadratic 221.9828 443.9656 0.5544 0.4565 T4+T2 Base 409.4662 818.9325 ––Base 138.5013 277.0025 –– Intercept 412.1464 824.2927 5.3603 0.0206 Intercept 151.9467 303.8934 26.8908 0.0000 Linear 419.8493 839.6987 15.4059 0.0001 Linear 152.1189 304.2378 0.3444 0.5573 Quadratic 423.9547 847.9094 8.2107 0.0042 Quadratic 157.0436 314.0873 9.8495 0.0017 T4+T3 Base 399.0775 798.1550 ––Base 219.0960 438.1919 –– Intercept 399.3430 798.6861 0.5311 0.4661 Intercept 213.9733 427.9465 10.2454 0.0014 Linear 405.2999 810.5998 11.9137 0.0006 Linear 213.9354 427.8708 0.0757 0.7832 Quadratic 410.9753 821.9506 11.3508 0.0008 Quadratic 208.8735 417.7469 10.1239 0.0015 T4+T4 Base 359.2168 718.4336 ––Base 184.1984 368.3967 –– Intercept 360.6681 721.3361 2.9025 0.0884 Intercept 191.1748 382.3495 13.9528 0.0002 Linear 371.8187 743.6373 22.3012 0.0000 Linear 191.9412 383.8824 1.5328 0.2157 Quadratic 373.2777 746.5554 2.9181 0.0876 Quadratic 192.7654 385.5308 1.6485 0.1992 T4+T5 Base 582.8682 1165.7364 ––Base 165.0094 330.0189 –– Intercept 585.4354 1170.8707 5.1343 0.0235 Intercept 186.6151 373.2301 43.2113 0.0000 Linear 597.0865 1194.1730 23.3023 0.0000 Linear 189.0802 378.1603 4.9302 0.0264 Quadratic 602.8275 1205.6550 11.4820 0.0007 Quadratic 190.8917 381.7835 3.6232 0.0570 T5+T1 Base 214.7018 429.4036 ––Base 276.4603 552.9207 –– Intercept 215.1207 430.2415 0.8379 0.3600 Intercept 273.6603 547.3207 5.6000 0.0180 Linear 216.3561 432.7123 2.4708 0.1160 Linear 268.3614 536.7229 10.5978 0.0011 Quadratic 217.4156 434.8312 2.1189 0.1455 Quadratic 265.2810 530.5620 6.1609 0.0131 T5+T2 Base 234.8543 469.7086 ––Base 322.6922 645.3844 –– Intercept 237.4781 474.9561 5.2475 0.0220 Intercept 321.9967 643.9933 1.3911 0.2382 Linear 237.4830 474.9661 0.0100 0.9205 Linear 319.2178 638.4355 5.5578 0.0184 Quadratic 238.2478 476.4955 1.5295 0.2162 Quadratic 318.9801 637.9602 0.4753 0.4905 T5+T3 Base 231.1113 462.2226 ––Base 23.1863 46.3725 –– Intercept 231.4704 462.9408 0.7182 0.3967 Intercept 21.2048 42.4096 3.9629 0.0465 Linear 231.4732 462.9464 0.0056 0.9403 Linear 19.4909 38.9819 3.4277 0.0641 194 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201

Quadratic 231.7253 463.4505 0.5041 0.4777 Quadratic 19.3968 38.7936 0.1882 0.6644 T5+T4 Base 188.2016 376.4033 ––Base 33.2223 66.4447 –– Intercept 191.7345 383.4690 7.0657 0.0079 Intercept 34.3733 68.7466 2.3019 0.1292 Linear 196.9853 393.9707 10.5017 0.0012 Linear 34.5044 69.0087 0.2621 0.6087 Quadratic 197.1502 394.3003 0.3296 0.5659 Quadratic 34.5417 69.0834 0.0747 0.7847 T5+T5 Base 459.8460 919.6920 ––Base 106.8791 213.7582 –– Intercept 463.1940 926.3879 6.6959 0.0097 Intercept 105.5306 211.0613 2.6970 0.1005 Linear 464.1382 928.2765 1.8885 0.1694 Linear 104.6552 209.3105 1.7508 0.1858 Quadratic 465.5581 931.1162 2.8397 0.0920 Quadratic 103.8211 207.6423 1.6682 0.1965

Appendix D. Model ﬁt comparisons for f0 curves between real and nonce disyllabic words that were classiﬁed as undergoing the spreading tone sandhi

Same model constructions as in Appendix C, except that only f0 patterns that have been classiﬁed as “Spreading” by a phonetically-trained Shanghai speaker are included in the analysis.

Base tones σ1 σ2

LogLik Deviance χ2(1) p LogLik Deviance χ2(1) p

T1+T1 Base 155.9024 311.8049 ––Base 33.1990 66.3980 –– Intercept 157.3064 314.6127 2.8078 0.0938 Intercept 32.0654 64.1309 2.2671 0.1321 Linear 157.6479 315.2957 0.6830 0.4086 Linear 30.3680 60.7359 3.3950 0.0654 Quadratic 158.3126 316.6252 1.3295 0.2489 Quadratic 28.0714 56.1428 4.5931 0.0321 T1+T2 Base 226.9220 453.8440 ––Base 72.1815 144.3631 –– Intercept 230.4701 460.9402 7.0963 0.0077 Intercept 71.8893 143.7785 0.5846 0.4445 Linear 231.8493 463.6985 2.7583 0.0968 Linear 71.1838 142.3675 1.4110 0.2349 Quadratic 233.7565 467.5131 3.8146 0.0508 Quadratic 70.7690 141.5380 0.8296 0.3624 T1+T3 Base 309.0907 618.1815 ––Base 73.0505 146.1010 –– Intercept 311.3913 622.7827 4.6012 0.0319 Intercept 73.0170 146.0340 0.0670 0.7958 Linear 311.3920 622.7840 0.0013 0.9708 Linear 67.5581 135.1162 10.9178 0.0010 Quadratic 311.6374 623.2749 0.4908 0.4836 Quadratic 65.0123 130.0246 5.0916 0.0240 T1+T4 Base 345.4102 690.8203 ––Base 35.9616 71.9231 –– Intercept 348.5594 697.1187 6.2984 0.0121 Intercept 35.8736 71.7472 0.1760 0.6749 Linear 348.8660 697.7320 0.6133 0.4336 Linear 35.8662 71.7324 0.0148 0.9031 Quadratic 359.6298 719.2596 21.5276 0.0000 Quadratic 34.5152 69.0304 2.7020 0.1002 T1+T5 Base 317.1673 634.3346 ––Base 46.1873 92.3746 –– Intercept 319.8937 639.7874 5.4528 0.0195 Intercept 46.5692 93.1385 0.7639 0.3821 Linear 323.6981 647.3962 7.6087 0.0058 Linear 53.4642 106.9283 13.7898 0.0002 Quadratic 328.1214 656.2428 8.8466 0.0029 Quadratic 57.2040 114.4081 7.4798 0.0062 T2+T1 Base 24.4374 48.8747 ––Base 79.7357 159.4715 –– Intercept 13.3018 26.6035 22.2712 0.0000 Intercept 81.3473 162.6945 3.2231 0.0726 Linear 13.1935 26.3869 0.2166 0.6417 Linear 83.9475 167.8950 5.2005 0.0226 Quadratic 11.3753 22.7506 3.6363 0.0565 Quadratic 87.9835 175.9671 8.0720 0.0045 T2+T2 Base 245.2670 490.5341 ––Base 156.6772 313.3543 –– Intercept 267.5737 535.1473 44.6132 0.0000 Intercept 153.0967 306.1934 7.1609 0.0075 Linear 268.1100 536.2201 1.0728 0.3003 Linear 153.0632 306.1265 0.0669 0.7958 Quadratic 273.0726 546.1452 9.9251 0.0016 Quadratic 150.7117 301.4233 4.7031 0.0301 T2+T3 Base 263.7960 527.5919 ––Base 110.0607 220.1213 –– Intercept 279.5350 559.0699 31.4780 0.0000 Intercept 109.7020 219.4040 0.7173 0.3970 Linear 279.6139 559.2277 0.1578 0.6912 Linear 108.2574 216.5147 2.8893 0.0892 Quadratic 281.5438 563.0875 3.8598 0.0495 Quadratic 102.9602 205.9205 10.5942 0.0011 T2+T4 Base 300.7854 601.5707 ––Base 82.8038 165.6075 –– Intercept 322.6066 645.2132 43.6425 0.0000 Intercept 86.0670 172.1340 6.5265 0.0106 Linear 322.9120 645.8240 0.6107 0.4345 Linear 91.5319 183.0639 10.9299 0.0009 Quadratic 322.9122 645.8244 0.0005 0.9824 Quadratic 91.9896 183.9792 0.9153 0.3387 T2+T5 Base 315.8283 631.6567 ––Base 337.8291 675.6582 –– Intercept 328.2395 656.4790 24.8223 0.0000 Intercept 339.2174 678.4349 2.7767 0.0956 Linear 328.3592 656.7183 0.2394 0.6247 Linear 342.7087 685.4173 6.9825 0.0082 Quadratic 330.7820 661.5640 4.8456 0.0277 Quadratic 342.9386 685.8772 0.4598 0.4977 T3+T1 Base 35.5936 71.1872 ––Base 28.5079 57.0158 –– Intercept 35.3676 70.7353 0.4520 0.5014 Intercept 33.5067 67.0133 9.9975 0.0016 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 195

Linear 33.3636 66.7272 4.0080 0.0453 Linear 33.6260 67.2520 0.2386 0.6252 Quadratic 32.7820 65.5640 1.1632 0.2808 Quadratic 36.7810 73.5619 6.3100 0.0120 T3+T2 Base 65.4391 130.8782 ––Base 5.5600 11.1200 –– Intercept 65.9909 131.9819 1.1037 0.2934 Intercept 14.1554 28.3109 17.1909 0.0000 Linear 67.3957 134.7914 2.8095 0.0937 Linear 14.1972 28.3943 0.0834 0.7727 Quadratic 67.8303 135.6606 0.8692 0.3512 Quadratic 14.3740 28.7481 0.3537 0.5520 T3+T3 Base 283.1860 566.3719 ––Base 44.1567 88.3135 –– Intercept 283.7396 567.4792 1.1073 0.2927 Intercept 48.8960 97.7920 9.4785 0.0021 Linear 284.2348 568.4697 0.9905 0.3196 Linear 48.9051 97.8102 0.0182 0.8927 Quadratic 287.0980 574.1961 5.7264 0.0167 Quadratic 48.9502 97.9004 0.0903 0.7638 T3+T4 Base 14.7166 29.4332 ––Base 81.2962 162.5924 –– Intercept 14.6403 29.2805 0.1527 0.6960 Intercept 78.3741 156.7482 5.8442 0.0156 Linear 14.4963 28.9926 0.2879 0.5916 Linear 78.0055 156.0110 0.7372 0.3906 Quadratic 7.9157 15.8315 13.1611 0.0003 Quadratic 78.0041 156.0082 0.0028 0.9579 T3+T5 Base 509.2307 1018.4614 ––Base 265.1966 530.3932 –– Intercept 509.2476 1018.4953 0.0338 0.8540 Intercept 269.8665 539.7329 9.3398 0.0022 Linear 509.3319 1018.6639 0.1686 0.6814 Linear 270.0107 540.0213 0.2884 0.5913 Quadratic 509.3632 1018.7264 0.0626 0.8025 Quadratic 270.1715 540.3431 0.3217 0.5706 T4+T1 Base 97.4982 194.9963 ––Base 32.8400 65.6799 –– Intercept 104.3152 208.6305 13.6342 0.0002 Intercept 34.1785 68.3570 2.6771 0.1018 Linear 104.4057 208.8113 0.1808 0.6707 Linear 39.0400 78.0801 9.7231 0.0018 Quadratic 105.6400 211.2801 2.4687 0.1161 Quadratic 39.0738 78.1476 0.0675 0.7950 T4+T2 Base 237.7817 475.5634 ––Base 126.7695 253.5389 –– Intercept 245.9490 491.8981 16.3347 0.0001 Intercept 131.8480 263.6959 10.1570 0.0014 Linear 247.9295 495.8591 3.9610 0.0466 Linear 132.7010 265.4020 1.7060 0.1915 Quadratic 248.7739 497.5478 1.6887 0.1938 Quadratic 134.2998 268.5995 3.1975 0.0737 T4+T3 Base 217.0528 434.1056 ––Base 87.8613 175.7226 –– Intercept 219.3757 438.7513 4.6457 0.0311 Intercept 84.5624 169.1248 6.5978 0.0102 Linear 225.2617 450.5234 11.7721 0.0006 Linear 84.2815 168.5630 0.5618 0.4536 Quadratic 226.1526 452.3053 1.7819 0.1819 Quadratic 81.3923 162.7847 5.7784 0.0162 T4+T4 Base 199.4080 398.8161 ––Base 136.3195 272.6391 –– Intercept 202.7435 405.4870 6.6709 0.0098 Intercept 138.1029 276.2059 3.5668 0.0589 Linear 208.7300 417.4601 11.9731 0.0005 Linear 139.4871 278.9741 2.7683 0.0961 Quadratic 208.7357 417.4715 0.0114 0.9150 Quadratic 139.7527 279.5054 0.5313 0.4661 T4+T5 Base 342.9964 685.9927 ––Base 145.4149 290.8298 –– Intercept 350.2851 700.5703 14.5775 0.0001 Intercept 154.8031 309.6063 18.7764 0.0000 Linear 355.2110 710.4219 9.8516 0.0017 Linear 157.6982 315.3964 5.7901 0.0161 Quadratic 355.7656 711.5312 1.1093 0.2922 Quadratic 157.7754 315.5507 0.1543 0.6945 T5+T1 Base 210.9883 421.9765 ––Base 155.1859 310.3718 –– Intercept 245.3380 490.6760 0.8040 0.7483 Intercept 151.5726 303.1451 7.2267 0.0072 Linear 246.4603 492.9206 2.2446 0.8455 Linear 151.2883 302.5766 0.5685 0.4508 Quadratic 247.6048 495.2096 2.2890 0.0712 Quadratic 151.6833 303.3665 0.0000 1.0000 T5+T2 Base 248.9684 497.9368 ––Base 311.7590 623.5180 –– Intercept 250.9822 501.9645 4.0277 0.0048 Intercept 310.8071 621.6143 1.9037 0.1677 Linear 251.5715 503.1430 1.1785 0.8162 Linear 307.2774 614.5548 7.0594 0.0079 Quadratic 253.0950 506.1900 3.0470 0.4087 Quadratic 307.4133 614.8265 0.0000 1.0000 T5+T3 Base 286.8311 573.6623 ––Base 2.0238 4.0477 –– Intercept 287.0973 574.1947 0.5324 0.4281 Intercept 4.0592 8.1184 4.0708 0.0436 Linear 287.3409 574.6817 0.4871 0.6353 Linear 7.1998 14.3996 6.2811 0.0122 Quadratic 287.3428 574.6856 0.0039 0.5006 Quadratic 7.2009 14.4018 0.0022 0.9623 T5+T4 Base 196.6955 393.3910 ––Base 63.4917 126.9835 –– Intercept 200.2094 400.4188 7.0279 0.0242 Intercept 64.8609 129.7218 2.7384 0.0980 Linear 205.4792 410.9584 10.5396 0.0014 Linear 64.9959 129.9918 0.2700 0.6033 Quadratic 205.6440 411.2880 0.3296 0.3337 Quadratic 65.1985 130.3969 0.4051 0.5245 T5+T5 Base 502.2306 1004.4612 ––Base 49.5944 99.1888 –– Intercept 504.8373 1009.6746 5.2135 0.0051 Intercept 48.5887 97.1774 2.0114 0.1561 Linear 505.3497 1010.6994 1.0248 0.0513 Linear 47.8198 95.6396 1.5379 0.2149 Quadratic 507.1044 1014.2087 3.5093 0.0169 Quadratic 47.4197 94.8395 0.8001 0.3711 196 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201

Appendix E. Model ﬁt comparisons for f0 curves between real and nonce disyllabic words that were classiﬁed as undergoing the spreading tone sandhi, corrected for vowel height

Same model constructions as in Appendix D, except that the dependent variable is the difference between the f0 on the target syllable and the average f0 for the height of the vowel (based on the lowest part of the syllable) used in the syllable in this position.

Base tones σ1 σ2

LogLik Deviance χ2(1) p LogLik Deviance χ2(1) p

T1+T1 Base 241.7305 483.4610 ––Base 56.4040 112.8080 –– Intercept 243.1519 486.3038 2.8428 0.0918 Intercept 55.2577 110.5154 2.2926 0.1300 Linear 243.4883 486.9766 0.6728 0.4121 Linear 53.4698 106.9395 3.5759 0.0586 Quadratic 244.1337 488.2675 1.2909 0.2559 Quadratic 51.6747 103.3493 3.5902 0.0581 T1+T2 Base 357.5182 715.0365 ––Base 105.7368 211.4736 –– Intercept 365.3463 730.6926 15.6561 0.0000 Intercept 105.5270 211.0540 0.4196 0.5171 Linear 365.4334 730.8667 0.1741 0.6765 Linear 99.3767 198.7534 12.3006 0.0005 Quadratic 365.5577 731.1154 0.2487 0.6180 Quadratic 99.3233 198.6466 0.1068 0.7438 T1+T3 Base 414.1977 828.3955 ––Base 110.4152 220.8303 –– Intercept 416.8801 833.7601 5.3647 0.0205 Intercept 110.3858 220.7717 0.0587 0.8086 Linear 416.8895 833.7789 0.0188 0.8910 Linear 104.8753 209.7507 11.0210 0.0009 Quadratic 417.0760 834.1521 0.3732 0.5413 Quadratic 102.2447 204.4895 5.2612 0.0218 T1+T4 Base 454.2628 908.5256 ––Base 88.8794 177.7588 –– Intercept 456.5631 913.1262 4.6006 0.0320 Intercept 87.1519 174.3038 3.4550 0.0631 Linear 456.9011 913.8022 0.6760 0.4110 Linear 83.9986 167.9973 6.3065 0.0120 Quadratic 460.2467 920.4933 6.6911 0.0097 Quadratic 81.7336 163.4672 4.5301 0.0333 T1+T5 Base 390.3801 780.7601 ––Base 2.8293 5.6586 –– Intercept 391.3339 782.6679 1.9077 0.1672 Intercept 1.7458 3.4915 2.1671 0.1410 Linear 393.4946 786.9892 4.3214 0.0376 Linear 6.7930 13.5860 17.0775 0.0000 Quadratic 399.7539 799.5078 12.5185 0.0004 Quadratic 8.5292 17.0584 3.4724 0.0624 T2+T1 Base 10.9342 21.8684 ––Base 68.4357 136.8714 –– Intercept 9.2351 18.4701 3.3983 0.0653 Intercept 69.1774 138.3548 1.4834 0.2232 Linear 8.4849 16.9698 1.5003 0.2206 Linear 69.7795 139.5589 1.2041 0.2725 Quadratic 7.9237 15.8475 1.1223 0.2894 Quadratic 71.9578 143.9156 4.3567 0.0369 T2+T2 Base 300.5851 601.1703 ––Base 164.7744 329.5489 –– Intercept 301.3095 602.6190 1.4487 0.2287 Intercept 163.7513 327.5026 2.0463 0.1526 Linear 301.3678 602.7356 0.1166 0.7327 Linear 162.5780 325.1559 2.3467 0.1256 Quadratic 301.3706 602.7413 0.0057 0.9400 Quadratic 162.2836 324.5673 0.5887 0.4429 T2+T3 Base 316.1509 632.3017 ––Base 123.4395 246.8790 –– Intercept 317.2588 634.5176 2.2159 0.1366 Intercept 115.2391 230.4782 16.4008 0.0001 Linear 317.5755 635.1510 0.6333 0.4261 Linear 100.4276 200.8552 29.6230 0.0000 Quadratic 317.5849 635.1698 0.0188 0.8908 Quadratic 98.5734 197.1468 3.7084 0.0541 T2+T4 Base 336.1164 672.2327 ––Base 22.6625 45.3251 –– Intercept 357.9376 715.8752 43.6425 0.0000 Intercept 25.6927 51.3855 6.0604 0.0138 Linear 358.2430 716.4860 0.6107 0.4345 Linear 31.3225 62.6451 11.2596 0.0008 Quadratic 358.2432 716.4864 0.0005 0.9824 Quadratic 31.8988 63.7976 1.1525 0.2830 T2+T5 Base 388.2331 776.4663 ––Base 294.1053 588.2106 –– Intercept 400.8991 801.7983 25.3320 0.0000 Intercept 296.9105 593.8211 5.6105 0.0179 Linear 401.0188 802.0377 0.2394 0.6247 Linear 303.4591 606.9181 13.0970 0.0003 Quadratic 403.4417 806.8833 4.8456 0.0277 Quadratic 303.5291 607.0581 0.1400 0.7083 T3+T1 Base 30.6852 61.3703 ––Base 14.1227 28.2453 –– Intercept 30.4592 60.9184 0.4520 0.5014 Intercept 18.9622 37.9244 9.6791 0.0019 Linear 28.4552 56.9104 4.0080 0.0453 Linear 20.0953 40.1905 2.2661 0.1322 Quadratic 27.8736 55.7471 1.1632 0.2808 Quadratic 20.3040 40.6081 0.4176 0.5181 T3+T2 Base 88.5676 177.1352 ––Base 14.5893 29.1787 –– Intercept 88.5682 177.1365 0.0013 0.9713 Intercept 21.0789 42.1578 12.9792 0.0003 Linear 90.6466 181.2933 4.1568 0.0415 Linear 21.7476 43.4951 1.3373 0.2475 Quadratic 90.6883 181.3766 0.0833 0.7729 Quadratic 27.1251 54.2502 10.7550 0.0010 T3+T3 Base 346.3424 692.6848 ––Base 78.4248 156.8496 –– Intercept 346.8960 693.7921 1.1073 0.2927 Intercept 83.0979 166.1959 9.3463 0.0022 Linear 347.3913 694.7826 0.9905 0.3196 Linear 83.1358 166.2715 0.0756 0.7833 Quadratic 350.2545 700.5090 5.7264 0.0167 Quadratic 81.6599 163.3199 0.0000 1.0000 T3+T4 Base 8.4499 16.8998 ––Base 136.3985 272.7970 –– Intercept 8.3736 16.7471 0.1527 0.6960 Intercept 133.8455 267.6910 5.1060 0.0238 Linear 8.2296 16.4592 0.2879 0.5916 Linear 133.7654 267.5307 0.1602 0.6889 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 197

Quadratic 1.6490 3.2981 13.1611 0.0003 Quadratic 132.1405 264.2810 3.2497 0.0714 T3+T5 Base 513.3129 1026.6259 ––Base 196.7865 393.5731 –– Intercept 514.1924 1028.3848 1.7589 0.1848 Intercept 201.6936 403.3872 9.8141 0.0017 Linear 514.3269 1028.6537 0.2689 0.6041 Linear 201.8991 403.7982 0.4110 0.5214 Quadratic 514.8131 1029.6262 0.9725 0.3241 Quadratic 202.6425 405.2850 1.4867 0.2227 T4+T1 Base 105.0954 210.1907 ––Base 35.3518 70.7035 –– Intercept 109.9663 219.9326 9.7419 0.0018 Intercept 37.4064 74.8128 4.1092 0.0427 Linear 110.5056 221.0112 1.0786 0.2990 Linear 43.2604 86.5208 11.7080 0.0006 Quadratic 112.1669 224.3337 3.3225 0.0683 Quadratic 43.2914 86.5829 0.0621 0.8033 T4+T2 Base 244.7540 489.5081 ––Base 114.7557 229.5115 –– Intercept 252.9214 505.8428 16.3347 0.0000 Intercept 121.0415 242.0829 12.5715 0.0004 Linear 254.9019 509.8038 3.9610 0.0466 Linear 122.5465 245.0930 3.0101 0.0827 Quadratic 255.7462 511.4925 1.6887 0.1938 Quadratic 124.2042 248.4084 3.3154 0.0686 T4+T3 Base 228.1197 456.2395 ––Base 84.8188 169.6377 –– Intercept 230.4426 460.8852 4.6457 0.0311 Intercept 79.1640 158.3280 11.3096 0.0008 Linear 236.3286 472.6572 11.7721 0.0006 Linear 78.6631 157.3261 1.0019 0.3168 Quadratic 237.2196 474.4391 1.7819 0.1819 Quadratic 75.6657 151.3314 5.9947 0.0143 T4+T4 Base 196.3100 392.6199 ––Base 141.0124 282.0248 –– Intercept 199.2815 398.5630 5.9431 0.0148 Intercept 141.6520 283.3040 1.2792 0.2580 Linear 203.7280 407.4560 8.8930 0.0029 Linear 142.0032 284.0063 0.7023 0.4020 Quadratic 203.8228 407.6455 0.1895 0.6633 Quadratic 142.1749 284.3498 0.3435 0.5578 T4+T5 Base 366.2498 732.4996 ––Base 152.7446 305.4892 –– Intercept 371.8858 743.7715 11.2719 0.0008 Intercept 155.8556 311.7112 6.2220 0.0126 Linear 374.2048 748.4096 4.6381 0.0313 Linear 157.9286 315.8573 4.1461 0.0417 Quadratic 374.2060 748.4121 0.0024 0.9607 Quadratic 157.9891 315.9781 0.1208 0.7281 T5+T1 Base 222.1879 444.3758 ––Base 115.6525 231.3049 –– Intercept 222.2259 444.4517 0.0759 0.7829 Intercept 114.1003 228.2006 3.1043 0.0781 Linear 222.2530 444.5060 0.0542 0.8158 Linear 112.5131 225.0261 3.1745 0.0748 Quadratic 223.5249 447.0499 2.5439 0.1107 Quadratic 110.1330 220.2661 4.7601 0.0291 T5+T2 Base 237.4164 474.8329 ––Base 290.8441 581.6882 –– Intercept 241.0191 482.0381 7.2052 0.0073 Intercept 288.7228 577.4457 4.2426 0.0394 Linear 241.8345 483.6690 1.6309 0.2016 Linear 285.1348 570.2695 7.1761 0.0074 Quadratic 242.7136 485.4272 1.7582 0.1848 Quadratic 283.3277 566.6554 3.6141 0.0573 T5+T3 Base 262.7016 525.4032 ––Base 72.6002 145.2004 –– Intercept 262.9304 525.8607 0.4575 0.4988 Intercept 74.6653 149.3306 4.1302 0.0421 Linear 262.9457 525.8913 0.0306 0.8612 Linear 77.7755 155.5511 6.2205 0.0126 Quadratic 262.9526 525.9053 0.0140 0.9059 Quadratic 77.7757 155.5514 0.0004 0.9848 T5+T4 Base 201.7920 403.5840 ––Base 79.8794 159.7589 –– Intercept 204.3324 408.6647 5.0807 0.0242 Intercept 80.2680 160.5360 0.7771 0.3780 Linear 209.4265 418.8530 10.1883 0.0014 Linear 81.8741 163.7482 3.2123 0.0731 Quadratic 209.8938 419.7877 0.9347 0.3337 Quadratic 82.3886 164.7772 1.0289 0.3104 T5+T5 Base 551.0529 1102.1058 ––Base 4.4616 8.9231 –– Intercept 554.1833 1108.3665 6.2607 0.0123 Intercept 6.0525 12.1050 3.1819 0.0745 Linear 555.4203 1110.8405 2.4740 0.1157 Linear 9.7904 19.5807 7.4757 0.0063 Quadratic 558.5867 1117.1735 6.3329 0.0119 Quadratic 9.8294 19.6588 0.0780 0.7800

Appendix F. Model ﬁt comparisons for f0 curves between nonce items with different syntactic structures

The dependent variable for the models is f0inz-score-transformed semi-tone values. The base model includes the linear and quadratic time terms and the participant and participant by structure (M–N vs. V–N) random effects on all time terms. The intercept, linear, and quadratic terms represent the effects of the successive addition of condition (with M–N as the baseline) and its interaction with time and time2. Terms that signiﬁcantly improve the previous model at the p<0.05 model are indicated in bold. R codes for the models: data.subset.base <- lmer(F0 (ot1+ot2)+(ot1+ot2|Speaker)+(ot1+ot2|Speaker:Structure), control¼lmerControl(optimizer¼"bobyqa"), data.subset, REML¼F) data.subset.0 <- lmer(F0 (ot1+ot2)+Structure+(ot1+ot2|Speaker)+(ot1+ot2|Speaker:Structure), control¼lmerControl(optimizer¼"bobyqa"), data.subset, REML¼F) data.subset.1 <- lmer(F0 (ot1+ot2)+Structure+ot1:Structure+(ot1+ot2|Speaker)+(ot1+ot2| Speaker:Structure), control¼lmerControl(optimizer¼"bobyqa"), data.subset, REML¼F) data.subset.2 <- lmer(F0 (ot1+ot2)+(ot1+ot2)n Structure+(ot1+ot2|Speaker)+(ot1+ot2|Speaker: Structure), control¼lmerControl(optimizer¼"bobyqa"), data.subset, REML¼F) 198 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201

Base tones σ1 σ2

LogLik Deviance χ2(1) p LogLik Deviance χ2(1) p

T1+T1 Base 167.3900 334.7799 ––Base 320.0158 640.0315 –– Intercept 167.7263 335.4526 0.6726 0.4121 Intercept 319.9220 639.8441 0.1875 0.6650 Linear 170.2692 340.5385 5.0859 0.0241 Linear 314.1458 628.2917 11.5524 0.0007 Quadratic 172.5501 345.1002 4.5617 0.0327 Quadratic 314.0280 628.0560 0.2357 0.6273 T1+T2 Base 278.8363 557.6726 ––Base 113.5854 227.1709 –– Intercept 278.8380 557.6760 0.0035 0.9531 Intercept 111.3803 222.7606 4.4102 0.0357 Linear 281.3899 562.7798 5.1037 0.0239 Linear 109.8774 219.7547 3.0059 0.0830 Quadratic 281.9182 563.8365 1.0567 0.3040 Quadratic 109.8257 219.6513 0.1034 0.7478 T1+T3 Base 140.9100 281.8199 ––Base 237.3354 474.6707 –– – Intercept 141.3570 282.7140 0.8941 0.3444 Intercept 236.2093 472.4185 2.2522 0.1334 Linear 147.5051 295.0102 12.2962 0.0005 Linear 231.3849 462.7698 9.6487 0.0019 Quadratic 147.6708 295.3416 0.3314 0.5648 Quadratic 222.8351 445.6701 17.0997 0.0000 T1+T4 Base 183.6591 367.3183 ––Base 237.1363 474.2726 –– Intercept 184.1188 368.2376 0.9193 0.3377 Intercept 238.4767 476.9533 2.6807 0.1016 Linear 191.4312 382.8623 14.6248 0.0001 Linear 238.9702 477.9404 0.9871 0.3205 Quadratic 191.8736 383.7473 0.8849 0.3469 Quadratic 238.9948 477.9895 0.0491 0.8246 T1+T5 Base 225.7142 451.4284 ––Base 39.8099 79.6198 –– Intercept 225.9214 451.8428 0.4144 0.5197 Intercept 50.4803 100.9605 21.3408 0.0000 Linear 230.5279 461.0559 9.2130 0.0024 Linear 51.5696 103.1393 2.1788 0.1399 Quadratic 232.6298 465.2596 4.2037 0.0403 Quadratic 51.7083 103.4165 0.2773 0.5985 T2+T1 Base 95.2891 190.5783 ––Base 323.6077 647.2154 –– Intercept 91.1359 182.2717 8.3065 0.0040 Intercept 323.5784 647.1568 0.0585 0.8088 Linear 80.8964 161.7929 20.4789 0.0000 Linear 315.3819 630.7637 16.3931 0.0000 Quadratic 80.5375 161.0750 0.7179 0.3968 Quadratic 315.3606 630.7211 0.0426 0.8365 T2+T2 Base 62.5470 125.0939 ––Base 40.8675 81.7351 –– Intercept 62.2131 124.4261 0.6678 0.4138 Intercept 33.3145 66.6290 15.1061 0.0001 Linear 52.4454 104.8907 19.5354 0.0000 Linear 28.1410 56.2819 10.3470 0.0013 Quadratic 52.4331 104.8662 0.0245 0.8756 Quadratic 26.6003 53.2007 3.0813 0.0792 T2+T3 Base 106.8484 213.6968 ––Base 365.1912 730.3825 –– Intercept 103.3151 206.6302 7.0667 0.0079 Intercept 355.0248 710.0497 20.3328 0.0000 Linear 93.5105 187.0209 19.6093 0.0000 Linear 351.5303 703.0606 6.9890 0.0082 Quadratic 93.5071 187.0142 0.0067 0.9349 Quadratic 349.0064 698.0127 5.0479 0.0247 T2+T4 Base 14.7064 29.4127 ––Base 75.6760 151.3521 –– Intercept 17.2135 34.4270 5.0143 0.0251 Intercept 75.7003 151.4006 0.0485 0.8256 Linear 24.1809 48.3617 13.9347 0.0002 Linear 79.0705 158.1409 6.7403 0.0094 Quadratic 24.9248 49.8496 1.4879 0.2225 Quadratic 79.7790 159.5580 1.4171 0.2339 T2+T5 Base 56.1550 112.3100 ––Base 45.4980 90.9959 –– Intercept 61.9958 123.9916 11.6816 0.0006 Intercept 37.5055 75.0110 15.9849 0.0000 Linear 70.2756 140.5513 16.5597 0.0000 Linear 37.2274 74.4547 0.5563 0.4557 Quadratic 70.6993 141.3986 0.8474 0.3573 Quadratic 32.8650 65.7301 8.7246 0.0031 T3+T1 Base 97.1666 194.3332 ––Base 236.5335 473.0670 –– Intercept 95.6281 191.2561 3.0771 0.0794 Intercept 235.7645 471.5290 1.5380 0.2149 Linear 67.7815 135.5630 55.6931 0.0000 Linear 225.1248 450.2496 21.2794 0.0000 Quadratic 67.7316 135.4632 0.0998 0.7520 Quadratic 223.0555 446.1110 4.1386 0.0419 T3+T2 Base 13.9992 27.9984 ––Base 9.1276 18.2553 –– Intercept 13.6609 27.3218 0.6766 0.4108 Intercept 9.0121 18.0242 36.2795 0.0000 Linear 13.7465 27.4931 54.8149 0.0000 Linear 10.7287 21.4574 3.4332 0.0639 Quadratic 15.2224 30.4448 2.9517 0.0858 Quadratic 14.8935 29.7871 8.3297 0.0039 T3+T3 Base 56.4460 112.8921 ––Base 81.0033 162.0066 –– Intercept 56.0839 112.1678 0.7243 0.3947 Intercept 70.1106 140.2211 21.7854 0.0000 Linear 33.5543 67.1085 45.0593 0.0000 Linear 68.7802 137.5604 2.6608 0.1028 Quadratic 33.5528 67.1055 0.0030 0.9564 Quadratic 59.0826 118.1652 19.3951 0.0000 T3+T4 Base 122.9692 245.9383 ––Base 183.8713 367.7426 –– Intercept 122.9882 245.9763 0.0380 0.8454 Intercept 185.7397 371.4794 3.7368 0.0532 Linear 142.9712 285.9425 39.9661 0.0000 Linear 189.2974 378.5948 7.1154 0.0076 Quadratic 143.8007 287.6015 1.6590 0.1977 Quadratic 190.6824 381.3648 2.7700 0.0960 T3+T5 Base 192.6669 385.3338 ––Base 196.4168 392.8337 –– Intercept 193.8724 387.7449 2.4111 0.1205 Intercept 206.3116 412.6232 19.7895 0.0000 Linear 223.5301 447.0602 59.3153 0.0000 Linear 212.9919 425.9839 13.3606 0.0003 Quadratic 223.5469 447.0939 0.0337 0.8543 Quadratic 216.4749 432.9499 6.9660 0.0083 T4+T1 Base 702.0199 1404.0398 ––Base 318.4079 636.8158 –– J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 199

Intercept 702.0215 1404.0429 0.0032 0.9550 Intercept 320.2794 640.5588 0.0000 1.0000 Linear 702.0230 1404.0460 0.0031 0.9559 Linear 316.8328 633.6657 6.8931 0.0087 Quadratic 706.1033 1412.2067 8.1607 0.0043 Quadratic 316.6461 633.2923 0.3734 0.5411 T4+T2 Base 644.2261 1288.4523 ––Base 32.8161 65.6323 –– Intercept 644.2264 1288.4528 0.0005 0.9823 Intercept 32.2236 64.4473 1.1850 0.2763 Linear 644.6245 1289.2489 0.7961 0.3723 Linear 31.5073 63.0147 1.4326 0.2313 Quadratic 644.6283 1289.2566 0.0077 0.9301 Quadratic 30.6895 61.3790 1.6357 0.2009 T4+T3 Base 701.7918 1403.5837 ––Base 208.7524 417.5048 –– Intercept 702.1918 1404.3836 0.8000 0.3711 Intercept 205.8908 411.7816 5.7232 0.0167 Linear 702.8716 1405.7431 1.3595 0.2436 Linear 205.8793 411.7586 0.0230 0.8794 Quadratic 702.6683 1405.3367 0.0000 1.0000 Quadratic 205.7332 411.4664 0.2922 0.5888 T4+T4 Base 692.0432 1384.0863 ––Base 89.8345 179.6691 ––– Intercept 692.0695 1384.1390 0.0527 0.8185 Intercept 90.4188 180.8376 1.1686 0.2797 Linear 692.1015 1384.2031 0.0640 0.8002 Linear 90.8506 181.7012 0.8636 0.3527 Quadratic 692.1615 1384.3230 0.1199 0.7291 Quadratic 91.0294 182.0588 0.3576 0.5499 T4+T5 Base 1082.9642 2165.9285 ––Base 105.4156 210.8312 –– Intercept 1086.3667 2172.7334 6.8049 0.0091 Intercept 110.2305 220.4610 9.6298 0.0019 Linear 1086.4451 2172.8903 0.1569 0.6921 Linear 110.2362 220.4724 0.0114 0.9151 Quadratic 1086.5833 2173.1667 0.2764 0.5991 Quadratic 110.3895 220.7789 0.3065 0.5798 T5+T1 Base 105.1735 210.3469 ––Base 276.4792 552.9584 –– Intercept 107.8164 215.6328 5.2858 0.0215 Intercept 276.2196 552.4392 0.5192 0.4712 Linear 110.7420 221.4839 5.8511 0.0156 Linear 267.7896 535.5792 16.8600 0.0000 Quadratic 112.2221 224.4443 2.9603 0.0853 Quadratic 267.7480 535.4960 0.0832 0.7730 T5+T2 Base 159.2853 318.5705 ––Base 185.6253 371.2506 –– Intercept 161.3766 322.7531 4.1826 0.0408 Intercept 179.9936 359.9872 11.2634 0.0008 Linear 175.1605 350.3211 27.5679 0.0000 Linear -170.2131 340.4262 19.5610 0.0000 Quadratic 176.3763 352.7526 2.4315 0.1189 Quadratic 169.5032 339.0064 1.4199 0.2334 T5+T3 Base 327.1391 654.2782 ––Base 53.4540 106.9080 –– Intercept 328.0452 656.0904 1.8121 0.1783 Intercept 48.4615 96.9229 9.9851 0.0016 Linear 348.3800 696.7600 40.6696 0.0000 Linear 38.9203 77.8406 19.0823 0.0000 Quadratic 350.4738 700.9476 4.1876 0.0407 Quadratic 37.1946 74.3893 3.4513 0.0632 T5+T4 Base 166.2355 332.4710 –– – Base 29.3220 58.6439 –– Intercept 166.5192 333.0385 0.5675 0.4513 Intercept 27.3825 54.7650 3.8789 0.0489 Linear 179.6590 359.3179 26.2795 0.0000 Linear 21.6661 43.3323 11.4327 0.0007 Quadratic 180.4103 360.8206 1.5027 0.2203 Quadratic 20.5296 41.0592 2.2731 0.1316 T5+T5 Base 255.6788 511.3576 ––Base 41.1109 82.2219 –– Intercept 258.5382 517.0764 5.7187 0.0168 Intercept 31.4359 62.8718 19.3501 0.0000 Linear 267.7862 535.5725 18.4961 0.0000 Linear 25.1663 50.3327 12.5391 0.0004 Quadratic 267.8440 535.6880 0.1155 0.7340 Quadratic 19.3379 38.6757 11.6569 0.0006

References

Albright, A. (2002). Islands of reliability for regular morphology: Evidence from Italian. Language, 78, 684–709. Albright, A., Andrade, A., & Hayes, B. (2001). Segmental environments of Spanish diphthongization. In A. Albright, & T. Cho (Eds.), UCLA Working Papers in Linguistics, 7 (Papers in Phonology, 5). Albright, A., & Hayes, B. (2003). Rules vs. analogy in English past tenses: A computational/experimental study. Cognition, 90,119–161. Balota, D. A., Pilotti, M., & Cortese, M. J. (2001). Subjective frequency estimates for 2,938 monosyllabic words. Memory and Cognition, 29, 639–647. Bates, D., Maechler, M., Bolker, B., & Walker, S. (2014). lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1-6. 〈http://CRAN.R-project.org/package¼lme4〉. Becker, M., Ketrez, N., & Nevins, A. (2011). The surfeit of the stimulus: Analytic biases ﬁlter lexical statistics in Turkish laryngeal alternations. Language, 87,84–125. Berent, I., Steriade, D., Lennertz, T., & Vaknin, V. (2007). What we know about what we have never heard: Evidence from perceptual illusions. Cognition, 104, 591–630. Berko, J. (1958). The child’s learning of English morphology. Word, 14, 150–177. Boersma, P. & Weenink, D. (2009). Praat: Doing phonetics by computer [computer program]. 〈http://www.praat.org/〉. Brunelle, M. (2009). Northern and Southern Vietnamese tone coarticulation: a comparative case study. Journal of the Southeast Asian Linguistics Society, 1,49–62. Bybee, J. (2001). Phonology and language use. Cambridge, UK: Cambridge University Press. Bybee, J., & Pardo, E. (1981). On lexical and morphological conditioning of alternations: A nonce-probe experiment with Spanish verbs. Linguistics, 19, 937–968. Cao, J.-F., & Maddieson, I. (1992). An exploration of phonation types in Wu dialects of Chinese. Journal of Phonetics, 20,77–92. Chang, Y.-C., & Hsieh, F.-F. (2012). Tonal coarticulation in Malaysian Hokkien: An typological anomaly?. The Linguistics Review, 29,37–73. Chao, Y.-R. (1948). Mandarin primer. Cambridge, MA: Harvard University Press. Chao, Y.-R. (1967). Contrastive aspects of the Wu dialects. Language, 43(1), 92–101. Chao, Y.-R. (1968). A grammar of spoken Chinese. Berkeley and Los Angeles: University of Berkeley Press. Chen, M. (2000). Tone sandhi: Patterns across Chinese dialects. Cambridge, UK: Cambridge University Press. Chen, Y.-Y. (2008). Revisiting the phonetics and phonology of Shanghai tone sandhi. In Proceedings of speech prosody 2008, (pp. 253-256). Campinas, Brazil. Chen, Y.-Y. (2011). What does phonology tell us about the phonetics of segment–f0 interaction?. Journal of Phonetics, 39, 612–625. Chen, Y.-Y., & Gussenhoven, C. (2008). Emphasis and tonal implementation in Standard Chinese. Journal of Phonetics, 36, 724–746. Chuang, C. -T, Chang, Y. -C., & Hsieh, F. -F. (2011). Productivity of Taiwanese tone sandhi redux. In W. -S. Lee, & E. Zee (Eds.), Proceedings of the 17th international conference of phonetic sciences (pp. 492–495). Hong Kong. Delvaux, V., & Soquet, A. (2007). The inﬂuence of ambient speech on adult speech productions through unintentional imitation. Phonetica, 64(2–3), 145–173. Duanmu, S. (1995). Metrical and tonal phonology of compounds in two Chinese dialects. Language, 71(2), 225–259. Duanmu, S. (2007). The phonology of Standard Chinese (2nd ed.). Oxford, UK: Oxford University Press. 200 J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201

Gandour, J., Potisuk, S., & Dechongkit, S. (1994). Tonal coarticulation in Thai. Journal of Phonetics, 22, 474–492. Gao, J.-Y., Hallé, P., Honda, K., Maeda, S., & Toda, M. (2011). Shanghai slack voice: Acoustic and EPGG Data. In Proceedings of the 17th international congress on phonetic sciences (pp. 719–722). Hong Kong. Goldinger, S. D. (1998). Echoes of echoes? An episodic theory of lexical access. Psychological Review, 105(2), 251–279. Han, M. S., & Kim, K.-O. (1974). Phonetic variation of Vietnamese tones in disyllabic utterances. Journal of Phonetics, 2, 223–232. ’t Hart, J., Collier, R., & Cohen, A. (1990). A perceptual study of intonation: An experimental-phonetic approach to speech perception. Cambridge, UK: Cambridge University Press. Haudricourt, A.-G. (1954). De l’origine des tons en Vietnamien. Journal Asiatique, 242,69–82. Hayes, B., & Londe, Z. (2006). Stochastic phonological knowledge: The case of Hungarian vowel harmony. Phonology, 23(1), 59–104. Hayes, B., & White, J. (2013). Phonological naturalness and phonotactic learning. Linguistic Inquiry, 44,45–75. Hayes, B., Zuraw, K., Siptár, P., & Londe, Z. (2009). Natural and unnatural constraints in Hungarian vowel harmony. Language, 85, 822–863. House, A. S., & Fairbanks, G. (1953). The inﬂuence of consonant environment upon the secondary acoustic characteristics of vowels. Journal of the Acoustical Society of America, 25, 268–277. Hsieh, H.-I. (1970). The psychological reality of tone sandhi rules in Taiwanese. Papers from the 6th meeting of the Chicago Linguistic Society (pp. 489–503)Chicago: Chicago Linguistic Society489–503. Hsieh, H.-I. (1975). How generative is phonology. In E. F. Koerner (Ed.), The transformational-generative paradigm and modern linguistic theory (pp. 109–144). Amsterdam: John Benjamins. Hsieh, H.-I. (1976). On the unreality of some phonological rules. Lingua, 38,1–19. Karlgren, B. (1915–1926). Études sur la phonologie chinoise (Archives D’Études Orientales vol. 15). Leiden: E. J. Brill. Kochanski, G., & Shih, C. (2003). Prosody modeling with soft templates. Speech Communication, 39,311–352. Laniran, Y. (1992). Intonation in tone languages: The phonetic implementation of tones in Yoruba (Ph.D. dissertation). Cornell University, Ithaca, NY. Maddieson, I. (1997). Phonetic universals. In W. J. Hardcastle, & J. Laver (Eds.), The handbook of phonetic sciences (pp. 619–639). Oxford, UK: Blackwell. Mirman, D. (2014). Growth curve analysis and visualization using R. Boca Raton: CRC Press. Moreton, E. (2008). Analytical bias and phonological typology. Phonology, 25,83–127. Nielsen, K. (2011). Speciﬁcity and abstractness of VOT imitation. Journal of Phonetics, 39, 132–142. Peng, S.-H. (1997). Production and perception of Taiwanese tones in different tonal and prosodic contexts. Journal of Phonetics, 25, 371–400. Peng, S.-H. (2000). Lexical versus ‘phonological’ representations of Mandarin sandhi tones. In M. Broe, & J. Pierrehumbert (Eds.), Language acquisition and the lexicon: Papers in laboratory phonology V (pp. 152–167). Cambridge, UK: Cambridge University Press. Peterson, G. E., & Lehiste, I. (1960). Duration of syllable nuclei in English. Journal of the Acoustical Society of America, 32, 673–703. Pierrehumbert, J. (2003). Probabilistic phonology: Discrimination and robustness. In R. Bod, J. Hay, & E. Jannedy (Eds.), Probabilistic linguistics (pp. 177–228). Cambridge, MA: MIT Press. Pierrehumbert, J. (2006). The statistical basis of an unnatural alternation. In L. Goldstein, D. H. Whalen, & C. Best (Eds.), Laboratory phonology 8, varieties of phonological competence (pp. 81–107). Berlin: Mouton de Gruyter. Pulleyblank, E. G. (1978). The nature of the Middle Chinese tones and their development to Early Mandarin. Journal of Chinese Linguistics, 6, 173–203. Potisuk, S., Gandour, J., & Harper, M. P. (1997). Contextual variations in trisyllabic sequences of Thai tones. Phonetica, 54,22–42. Qian, N.-R. (1997). ). Shanghaihua yufa (A grammar of Shanghai). Shanghai: Shanghai Renmin Chubanshe (Shanghai People’s Press). R Core Team (2014). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing (Version 3.1.0). Ren, N.-Q. (1992). Phonation types and consonant distinctions: Shanghai Chinese (Ph.D. dissertation). University of California, Los Angeles. Rietveld, T., & Chen, A.-J. (2006). How to obtain and process perceptual judgements of intonational meaning. In S. Sudhoff, D. Lenortová, R. Meyer, S. Pappert, P. Augurzky, I. Mleinek, N. Richter, & J. Schieβer (Eds.), Methods in empirical prosody research (pp. 283–319). Berlin: Walter de Gruyter. Rose, P. (1987). Considerations in the normalization of the fundamental frequency in linguistic tone. Speech Communication, 6, 343–351. Selkirk, E., & Shen, T. (1990). Prosodic domains in Shanghai Chinese. In S. Inkelas, & D. Zec (Eds.), The phonology-syntax connection (pp. 313–337). Chicago: University of Chicago Press. Shen, T. (1981). Laopai Shanghai fangyan de liandu biandiao (Tone sandhi in Old Shanghai). Fangyan (Dialects), 1981, 131–144. Shen, X. S. (1990). Tonal coarticulation in Mandarin. Journal of Phonetics, 18, 281–295. Shen, Z.-W., & Wang, W. S. (1995). Wuyu zhuoseyin de yanjiu – Tongji shang de fenxi he lilun shang de kaolü (A study of voiced stops in thje Wu dialects – Statistical analysis and theoretical considerations). In E. Zee (Ed.), Wuyu Yanjiu (Studies of the Wu Dialects) (pp. 219–238). Hong Kong: New Asia Books. Sherard, M. (1972). Shanghai phonology (Ph.D. dissertation). Ithaca, NY: Cornell University. Shih, C.-L. (1986). The prosodic domain of tone sandhi in Chinese (Ph.D. dissertation). San Diego: University of California. Tagliaferri, B. (2010). Perception Research Systems: Paradigms [Computer program]. 〈http://www.paradigmexperiments.com/〉 Retrieved March 2010. Takahashi, Y. (2013). The phonological structure of Shanghai tone sandhi (Ph.D. dissertation). Tokyo, Japan: Tokyo University of Foreign Studies. Tilsen, S. (2009). Subphonemic and cross-phonemic priming in vowel shadowing: Evidence for the involvement of exemplars in production. Journal of Phonetics, 37(3), 276–296. Toda, T. (1990). Shanghai tonal phonology “rightward spreading”? Some arguments based on acoustic evidence. In Australasian Speech Science and Technology Association (Ed.), Proceedings of the 3rd international conference on speech science and technology (pp. 382–387). Melbourne: Telecom Research Labs. Wang, H. S. (1993). ). Taiyu biandiao de xinli texing” (On the psychological status of Taiwanese tone sandhi). Tsinghua Xuebao (Tsinghua Journal of Chinese Studies), 23, 175–192. Wang, Y.-Z. (2011). Acoustic measurements and perceptual studies on initial stops in Wu dialects – Take Shanghainese for example (Ph.D. dissertation). Hangzhou, China: Zhejiang University. Whalen, D., & Levitt, A. (1995). The intrinsic pitch of vowels. Journal of Phonetics, 24, 349–366. Wickham, H. (2009). ggplot2: Elegant graphics for data analysis. New York: Springer. Wilson, C. (2006). Learning phonology with substantive bias: An experimental and computational study of velar palatalization. Cognitive Science, 30(5), 945–982. Xu, B.-H., & Tang, Z.-Z. (1988). Shanghai shiqu fangyan zhi (A grammar of inner city Shanghai). Shanghai: Shanghai Jiaoyu Chubanshe (Shanghai Education Press). Xu, B.-H., Tang, Z.-Z., & Qian, N.-R. (1981). Xinpai Shanghai fangyan de liandu biandiao (Tone sandhi in New Shanghai). Fangyan (Dialects), 1981, 145–155. Xu, B.-H., & Tao, H. (1997). Shanghai fangyan cidian (Shanghai dialect dictionary). Jiangsu Jiaoyu Chubanshe (Jiangsu Educational Press). Xu, Y. (1997). Contextual tonal variations in Mandarin. Journal of Phonetics, 25,61–83. Xu, Y. (1999). Effects of tone and focus on the formation and alignment of F0 contours. Journal of Phonetics, 27,55–105. Xu, Y. (2005). Speech melody as articulatorily implemented communicative functions. Speech Communication, 46, 220–251. Xu, Y. (2005–2011). ProsodyPro.praat, 〈http://www.phon.ucl.ac.uk/home/yi/ProsodyPro/〉 Retrieved September 2010. Yan, H.-B. & Zhang, J. (in press). Pattern substitution in Wuxi tone sandhi and its implication for phonological learning. International Journal of Chinese Linguistics. Yip, M. (1990). The tonal phonology of Chinese. New York: Garland. Yu, A. C. L. (2007). Understanding near merger: The case of morphological tone in Cantonese. Phonology, 24, 187–214. Yuan, J.-H., & Chen, Y.-Y. (2014). 3rd tone sandhi in Standard Chinese: A corpus approach.. Journal of Chinese Linguistics, 42(1), 218–236. Yue-Hashimoto, A. O. (1987). Tone sandhi across Chinese dialects. In Chinese Language Society of Hong Kong. Wang Li memorial volumes, English volume (pp. 445–474)Hong Kong: Joint Publishing Co445–474 (ed.). Zee, E., & Maddieson, I. (1980). Tones and tone sandhi in Shanghai: Phonetic evidence and phonological analysis. Glossa, 14,45–88. Zhang, J. (2002). The effects of duration and sonority on contour tone distribution: a typological survey and formal analysis. New York: Routledge. Zhang, J. (2007). A directional asymmetry in Chinese tone sandhi systems. Journal of East Asian Linguistics, 16, 259–302. Zhang, J. (2010). Issues in the analysis of Chinese tone. Language and Linguistics Compass, 4(12), 1137–1153. Zhang, J. (2014a). Tones, tonal phonology, and tone sandhi. In C.-T. J. Huang, Y.-H. A. Li, & A. Simpson (Eds.), The handbook of Chinese linguistics (pp. 443–464). Oxford, UK: Wiley- Blackwell. Zhang, J., & Lai, Y.-W. (2008). Phonological knowledge beyond the lexicon in Taiwanese double reduplication. In Y.-C. Hsiao, H.-C. Hsu, L.-H. Wee, & D.-A. Ho (Eds.), Interfaces in Chinese phonology: Festschrift in honor of Matthew Y. Chen on his 70th birthday (pp. 183–222). Taipei: Academia Sinica. Zhang, J., & Lai, Y.-W. (2010). Testing the role of phonetic knowledge in Mandarin tone sandhi. Phonology, 27(1), 153–201. Zhang, J., Lai, Y.-W., & Sailor, C. (2011). Modeling Taiwanese speakers’ knowledge of tone sandhi in reduplication. Lingua, 121(2), 181–206. Zhang, J., & Liu, J. (2011). Tone sandhi and tonal coarticulation in Tianjin Chinese. Phonetica, 68(3), 161–191. Zhang, J. & Liu, J. (in press). The productivity of variable disyllabic tone sandhi in Tianjin Chinese.Journal of East Asian Linguistics. http://dx.doi.org/10.1007/s10831-015-9135-0. Zhu, X.-N. (1999). Shanghai tonetics.München: Lincom Europa. J. Zhang, Y. Meng / Journal of Phonetics 54 (2016) 169–201 201

Zhu, X.-N. (2004). ). Jipin guiyihua – ruhe chuli shengdiao de suiji chayi? (F0 normalization — How to deal with between-speaker tonal variations?). Yuyan Kexue (Linguistic Sciences), 3 (2), 3–19. Zhu, X.-N. (2006). A grammar of Shanghai Wu.München: Lincom Europa. Zuraw, K. (2000). Patterned exceptions in phonology (Ph.D. dissertation). Los Angeles: University of California. Zuraw, K. (2003). Probability in language change. In R. Bod, J. Hay, & S. Jannedy (Eds.), Probabilistic linguistics (pp. 139–176). Cambridge, MA: MIT Press. Zuraw, K. (2007). The role of phonetic knowledge in phonological patterning: corpus and survey evidence from Tagalog inﬁxation. Language, 83, 277–316.