2.1 Cue weighting

Speech sound contrasts simultaneously incorporate many co-varying acoustic cues. For example, American English phonologically voiced and voiceless stops can differ on dimensions such as voice onset time (VOT), fundamental frequency (f0) at the onset of the following vowel, and presence/absence of prevoicing. Lisker (1986) notes that at least 16 co-varying acoustic cues are involved in this contrast. While speakers and listeners can make use of many phonetic dimensions in contrast realization, cues differ in their relative strength. Cue weighting quantifies the degree to which an individual dimension contributes to overall perception or production of a contrast.

Weighting cues in production data is frequently done by applying a classification algorithm (e.g., discriminant analysis, logistic regression) where the relevant cues are predictors (e.g., Garellek & White, 2015; Kim & Clayards, 2019). Strength of each predictor is taken to be a metric of cue weight. Differences in cue weighting patterns for the same phonological contrast have been observed in production between native speakers of the same language (Shultz, Francis, & Llanos, 2012), native and non-native speakers (Schertz, Cho, Lotto, & Warner, 2015), non-native speakers with different levels of L2 exposure (Kong & Yoon, 2013), and speakers of a language undergoing sound change (Bang, Sonderegger, Kang, Clayards, & Yoon, 2018; Coetzee, Beddor, Shedden, Styler, & Wissing, 2018; Kuang & Cui, 2018).

Cue weighting in perception is typically examined with perceptual experiments where a single phonetic dimension is manipulated to determine effects on listener discrimination and/or categorization. Cue weighting in perception “tends to reflect community production norms on a broad level” (Schertz & Clare, 2020, p.2), though relative differences in perceptual cue weighting are still observed among individual speakers of the same language (e.g., Chandrasekaran, Sampath, & Wong, 2010; Idemaru, Holt, & Seltman, 2012; Kong & Edwards, 2016; Clayards, 2018). In addition, relative perceptual weights can be manipulated in the lab by altering properties of the training data to which participants are exposed, including extent of within-category variability (Holt & Lotto, 2006; Clayards, Tanenhaus, Aslin, & Jacobs, 2008).

For the purposes of the current proposal, the crucial distinction is between primary cues, the cues with the highest relative weight, and secondary cues, the cues with lower weights than the primary cue. While individual variation is to be expected in relative weights of secondary cues, based on previous work on Hindi and English stops (summarized in Section 3) the primary versus secondary status of cues to stop voicing is expected to be consistent across speakers within each language (i.e., individual variation in which cues are primary is not expected).¹ In this paper, I analyze multiple cues to stop voicing in Hindi and English to demonstrate that cue status (primary versus secondary) in each language must be considered to account for differences in extent and structure of phonetic variability.

2.2 Phonetic variability

There is a large body of work on sources of phonetic variability in speech production, which include speaking rate (e.g., Baese-Berk & Morrill, 2015), recent language exposure (e.g., Babel, 2012; Nielsen, 2011), word frequency (e.g., Gahl, 2008; Warner & Tucker, 2011), and speaking style (e.g., Krause & Braida, 2004), to name only a few. While there are many factors which may contribute to phonetic variability in production, this section focuses specifically on individual differences, variability in lab speech, and hypotheses about extent of within-category phonetic variability.

2.3 Dispersion Theory

Dispersion Theory (DT; Liljencrants & Lindblom, 1972; Lindblom, 1986; Schwartz, Boë, Vallée, & Abry, 1997) was originally formulated to make predictions about the relative typological frequency of vowel inventories. The intuition behind DT is that vowel spaces are optimized for perceptual distinction. Liljencrants and Lindblom (1972) propose maximal contrast as an organizing principle in vowel inventories, defining the vowel space using two phonetic dimensions: F1 and F2’, which is a combination of F2 and F3. Their model correctly predicts cross-linguistic frequency of /i a u/ for three-vowel inventories but has more discrepancies with predictions for larger inventories.

Several updates have been made to the original formulation of DT to address these and other discrepancies. Lindblom (1986) adds multiple revisions including the concept of sufficient instead of maximal dispersion. Dispersion from sufficient contrast predicts that languages with more phonological categories should occupy an overall larger phonetic space and have tighter categories (i.e., less within-category variation) within that space. Schwartz et al. (1997) later expand the dispersion calculation used in Liljencrants and Lindblom (1972) to include intra-vowel spectral information in addition to inter-vowel distances. Their Dispersion-Focalization Theory (DFT) includes an energy function with two perceptual components: a global dispersion term quantifying inter-vowel distance, and a local focalization term quantifying intra-vowel spectral salience based on proximity of formants.

DT ideas have also been formulated in Optimality Theory (OT; Prince & Smolensky, 1993/2004) with the use of constraints that explicitly demand distance between members of a phonological inventory (e.g., Flemming, 1996). The goals of the system are to maximize distinctiveness of contrast while minimizing articulatory effort and maximizing the number of total contrasts in the system. OT formulations of DT have successfully been used to model palatalization (Padgett, 2001) and vowel reduction (Padgett & Tabain, 2005; Flemming, 2004), but the approach has been criticized for being ‘teleological’ (Boersma & Hamann, 2008). Constraints that demand types of contrast must evaluate entire inventories/languages, departing from traditional formulations of OT.

Other approaches have attempted to derive emergent dispersion effects, rather than explicitly demanding dispersion by grammatical or other factors. For example, Boersma and Hamann (2008) show that when production and perception are modeled with bidirectional phonetic cue constraints, dispersion emerges without constraints specifically demanding it. They note that change towards a more dispersed inventory has been observed in diachronic change between medieval and present Polish sibilants. Hall (2011) uses contrastive feature specification to show that dispersion effects can emerge when phonological representations only specify contrastive features. Predictable features are then enhanced during phonetic realization, creating a dispersion effect.

Engstrand and Krull (1994) extend the ideas of DT to account for cross-linguistic differences in durational correlates of vowel quantity, departing from previous DT work focusing largely on vowel quality. They use the DT idea of sufficient contrast to explain their results showing that Estonian and Finnish speakers preserve the durational correlate of quantity more relative to Swedish speakers. This is because quantity contrasts are mainly based on duration in Finnish and Estonian while Swedish quantity is also correlated with vowel quality and diphthongization. They argue that contrasts which use a highly exploited feature dimension should require more ‘precise signal information’ relative to contrasts on a less exploited dimension. This is supported with their data, which shows more between-category dispersion in the durational dimension in Estonian and Finnish relative to Swedish. I make a similar argument in this paper, but concerning within-category dispersion rather than between-category dispersion. There should be relatively less within-category variation when a dimension is employed as a primary cue (i.e., a highly exploited feature dimension) relative to when it is employed as a secondary cue.

3.1 Hindi background

Hindi is one of several Indo-Aryan languages which exhibit a four-way laryngeal contrast on stops (Table 1). Dutta (2007) cites UPSID (Maddieson & Disner, 1984) which contains ten languages from six families with the four-way contrast. In Hindi, the four-way contrast occurs at four places of articulation: bilabial, dental, retroflex, and velar. Voice onset time (VOT) has frequently been analyzed as a phonetic correlate to these stop contrasts (Lisker & Abramson, 1964; Abramson & Lisker, 1967; Poon & Mateer, 1985). VOT is a duration measure of the onset of voicing relative to the release of the stop occlusion, and is often implemented as a continuum of negative and positive values. Lead voicing before the stop closure is coded as negative VOT and lag voicing which begins after the stop closure is coded as positive VOT (Lisker & Abramson, 1964; Cho & Ladefoged, 1999).

Using the single VOT measure for lead and lag voicing has been challenged. In particular, VOT has been recognized as inadequate for languages like Hindi which have stops that are produced with lead voicing and aspiration (Lisker & Abramson, 1964; Schiefer, 1986; Dixit, 1989). Mikuteit and Reetz (2007) use data from East Bengali (another language with a four-way contrast) to argue that lead voicing and lag voicing should not be considered part of the same continuum. They instead propose separate duration measures of after closure time (duration from release to onset of voicing; lag time), onset voicing (start of glottal pulsing to release in initial stops), and connection voicing (closure duration in medial stops).

Following this analysis, I consider lag time (traditionally known as positive VOT) to be a separate phonetic dimension from lead time (traditionally known as negative VOT). In this paper, I use lag time to indicate the duration between the stop burst and onset of voicing, closure duration to indicate the duration of the stop closure, and closure voicing to indicate the duration of periodic voicing during the stop closure. See Section 6 for further discussion of how voicing was measured and analyzed in the production experiment.

In terms of distinctive features, Hindi is typically described as fully crossing all values of two features [± voice] and [± spread glottis] (Dutta, 2007), shown in Table 2. I will refer to instances of [+voice] as phonologically voiced stops and instances of [+spread glottis] as phonologically aspirated stops. There is some debate in the literature about the exact feature specification of the voiced aspirates (e.g., Benguerel & Bhatia, 1980; Dixit, 1989; Dutta, 2007). There is also debate about whether these features should be binary or privative, a controversy which is not specific to Hindi (e.g., Honeybone, 2005; Schwarz, Sonderegger, & Goad, 2019). The questions examined in this paper do not hinge on any particular feature representations and I discuss implications for feature specification in Section 6.4.1.

3.2 English background

American English has two contrasting stop consonants at three places of articulation: bilabial, alveolar, and velar. These can be seen in the English consonant inventory given in Table 3. In American English, lag time is the primary cue to the stop contrast and other phonetic cues such as F0 frequently co-vary with lag time (e.g., Lisker & Abramson, 1967; Keating, 1984; Lisker, 1986). Because lag time is the primary cue, English is often considered to be an aspirating language instead of a true voicing language. Despite this, phonemic representations of English typically use the IPA symbols for voiceless and voiced stops /t d/.

There is some disagreement on which phonological features should be used to distinguish English stops. Laryngeal realism takes the position that phonological features should reflect phonetic realization in word-initial (or another prominent) position (e.g., Jessen & Ringen, 2002; Honeybone, 2005; Beckman, Jessen, & Ringen, 2013). Under this view, the feature distinguishing the two English stops is [spread glottis] (features are also typically privative in laryngeal realism). The laryngeal relativist view takes a more abstract approach focusing on cross-linguistic similarities. In this view, two-way stop contrasts are typically represented with [(±)voice], and phonetic implementation can differ across languages (e.g., Keating, 1984; Kingston & Diehl, 1994; Lombardi, 1994; Cyran, 2011). Table 4 shows these potential representations of the English stop phones and their common phonetic realizations in word-initial position. In this paper, I will assume the [±voice] analysis and revisit the question of feature representation in Section 6.4.1. In all discussion that follows, I refer to the English short lag stops /b d ɡ/ as phonologically voiced and the English long lag stops /p t k/ as phonologically voiceless.

Despite the use of voiceless lag time as the primary cue to stop voicing in English, many studies have reported prevoicing on English phonologically voiced stops, which is assumed to be the primary cue to voicing in true voicing languages. Flege (1982) summarizes previous studies (Lisker & Abramson, 1964; Lorge, 1967; Zlatin, 1974; Smith, 1978; Westbury & Niimi, 1979) in which 20–57% of English stops are produced with prevoicing, and also reports results in which more than half of all phonologically voiced stops produced by ten male speakers of American English were produced with prevoicing. Docherty (1992) reports prevoicing incidence from five male speakers of British English. In that study, on average, duration of voicing during stop closures was 51% (of closure) for [b], 58% for [d], and 66% for [ɡ]. Deterding and Nolan (2007) also find similar results in a later study of seven British English speakers.

In more recent work, Davidson (2016) documents prevoicing on American English stops, and finds prevoicing variation in connected read speech to be influenced by linguistic factors such as adjacent sounds and lexical stress. There is also a growing body of work in sociolinguistic literature documenting prevoicing in Southern American English varieties (in utterance-initial and medial contexts), sometimes with higher incidence among male and African-American speakers (Jacewicz, Fox, & Lyle, 2009; Elston et al., 2016; Herd, Torrence, & Carino, 2016; Hunnicutt & Morris, 2016). Overall, previous work on production of stop voicing in American English suggests that use of prevoicing is common but inconsistent, with potentially higher incidence in particular varieties and phonological contexts.

Prevoicing has been shown to influence perception of English stop voicing contrasts in syllable-final position (Hillenbrand, Ingrisano, Smith, & Flege, 1984; Hogan & Rozsypal, 1980; Wardrip-Fruin & Peach, 1984). Pisoni, Aslin, Perey, and Hennessy (1982) also demonstrate that English speakers can learn to reliably discriminate between word-initial prevoiced stops and voiceless unaspirated stops in the lab with only a few minutes of exposure training. While lag time is consistently shown to be the primary cue to the word-initial voicing contrast in American English (e.g., Lisker & Abramson, 1967; Keating, 1984; Lisker, 1986), these results suggest that prevoicing has at least some degree of perceptual relevance as a secondary cue.

3.3 Predictions for the present study

In this section, I compare the predictions of Contrast-Dependent Variation with an application of Lindblom’s (1986) DT hypothesis about the inverse relationship between inventory size and extent of phonetic variability. The two hypotheses are summarized in Table 5.

The main assumption behind DT is that phonetic realizations are optimized to preserve perceptual distinction, which can be done by increasing between-category dispersion and avoiding category overlap. Under this framework, Lindblom (1986) makes a concrete prediction about the relationship between within-category variation and inventory size. As in most work in DT, the focus is on vowels, but the general assumption of preserving perceptual distinction could also be applied to consonants (see Section 2.3 for a review of previous work in this area). In extending Lindblom’s hypothesis about variation to consonants, we might consider the stop inventory to be the relevant ‘system’ as Lindblom considers the vowel inventory to be the relevant ‘system’ (Lindblom, 1986, p. 33). Lindblom’s hypothesis relies on inventory size to make predictions, and does not distinguish between phonetic dimensions. If the stop inventory is understood to be the relevant system, Lindblom’s hypothesis predicts less variation in Hindi relative to English because Hindi has a larger stop inventory. One particular prediction that could be drawn from this is that we would expect voiceless aspirated stops in Hindi to vary less in lag time. Expected results under this prediction are shown in Figure 1.

Figure 1

Predicted voiceless lag time distributions for two hypotheses in Hindi and English. Distributions pictured are schematic examples of possible distributions predicted under each hypothesis, generated with the rnorm function from the stats package in base R (R Core Team, 2013).

I advocate for an alternative approach which considers individual phonetic dimensions to be the relevant ‘system,’ using relative differences in cue weight to make predictions about relative differences in extent of variability (Contrast-Dependent Variation; Hauser, 2019). For a given phonetic dimension, we expect less variability in languages where that dimension is used as a primary cue relative to languages where that dimension is used as a secondary cue. Under this hypothesis, no difference in lag time variation is expected between the two languages. This is because because both languages employ lag time as a primary cue for distinguishing short and long lag stops in word-initial utterance-medial position, the context of elicitation in this study. However, we do expect more voicing variation in English relative to Hindi, as Hindi uses closure voicing as a primary cue in this context. While closure voicing does co-vary with lag time (and other cues) as a secondary cue for English stop contrasts, English does not use closure voicing as a primary cue to distinguish any phonological contrasts. The primary cues of Hindi and English are sketched in Table 6.

4.1 Participants

All speakers were between the ages of 18-30 and recruited from student populations of the University of Massachusetts Amherst. Most of the English speakers were undergraduates enrolled in introductory linguistics courses and most of the Hindi speakers were graduate students in various fields. In the first round of data collection, nine speakers of each language were recorded.

The task was a production task which involved reading phrases off a computer screen. Therefore, native speakers with poor reading skills spoke unnaturally during the task and produced many speech errors. Any participants who expressed difficulty with the task and/or paused before the stimulus leaving silence for more than 1.5 seconds on at least 75% of the phrases were removed from the analysis. Five Hindi speakers and one English speaker were excluded according to these criteria. Two Hindi speakers were additionally removed from the analysis because they were L2 speakers of Hindi (which was determined by their answers to a demographic questionnaire about language background). Two English speakers were additionally removed because they did not complete the task. After exclusions, data from two Hindi speakers from the first round of data collection were retained.

To replace the Hindi speakers which were excluded in the first round, a second round of data collection was conducted with a few adjustments. The call for participants was circulated only in Hindi orthography to ensure the participants were comfortable with reading in addition to speaking. Additionally, the experimenter was always a native speaker of Hindi who only spoke Hindi to the participants throughout the experiment. This helped in resolving confusion among the participants about L1/L2 status of Hindi before they participated. These were the only differences in the procedure of the experiment between the first round and the second round of data collection. The two speakers whose data were retained from the first round of collection did not systematically differ in extent of lag time or closure voicing variance relative to those in the second round of collection. After the second round of data collection, recordings from six speakers of each language were available for analysis.

4.2 Stimuli

The goal was for stimuli to be as similar as possible between the two languages. The stimuli were C₁VC₂ words and non-words where C₁ was a stop and V was one of [i a u].⁴ The coda consonant of the stimulus (C₂) was in most cases a stop. If there were no stops available that could make a phonotactically natural word or non-word, then a fricative was used. If there were no fricatives available, then a sonorant was used. Eliciting only monosyllabic words avoided any effects of stress placement. All stimuli were recorded in a uniform carrier phrase: “Say X again” in English and “Dobara X doharao” (repeat X again) in Hindi. The carrier phrases placed the target words in focused environments in both languages. The stimuli were all developed in consultation with native speakers to assure phonotactic wellformedness.

Real words and non-words were used in both the Hindi and English stimuli. Hindi stimuli were crossed according to the following factors: consonant (16 levels) × vowel context (3 levels) × word status (2 levels: word/non-word) for a total of 96 distinct stimuli. English stimuli were crossed according to: consonant (6 levels) × vowel context (3 levels) × word status (4 levels: high frequency/low frequency/non-word/has C₁ minimal pair) for a total of 72 distinct stimuli. Example stimuli are given in Table 7.

The English stimuli were crossed according to word frequency and minimal pair status, using data obtained from the English Lexicon Project (Balota et al., 2007). At the time of initial data collection, similar lexical statistics were not readily available for Hindi, so quantitative word frequency data was only included in the initial English stimuli selection. However, such materials have become available in the time since data collection, allowing for a post-hoc analysis of word frequency effects in both languages, using Hindi data from WorldLex (Gimenes & New, 2016). The analyses of lexical statistics in both languages showed no significant effect of word status or word frequency on lag time or closure voicing (statistical models are provided in the Appendix). Therefore, I do not include word status or frequency statistics as factors in any of the analyses that follow. Statistical models do include item as a random effect, when appropriate, to account for any idiosyncratic effects of particular words.

4.3 Recording

The participants were all recorded in a sound-attenuated booth using Audacity software (Audacity Team, 1999–2021). The recordings were done using an M-Audio Fast Track Pro Mobile Audio Interface and a Shure SM10A head-worn microphone. The recordings were sampled at a rate of 44.1 kHz with a bit depth of 16. The participants were presented with stimuli in the relevant orthography on a laptop computer inside the booth. They were asked to produce the phrases as naturally as possible. All experimenters were trained to give feedback which encouraged natural production.⁵ The stimuli were recorded in four separate blocks, each with a different random order, totaling four repetitions of each stimulus for analysis.

The recordings from each speaker were first scanned by the author and/or a native speaker research assistant for speech errors. After speech error exclusions, there were a total of 3663 tokens available for analysis. The recordings were force aligned using the Montreal Forced Aligner (McAuliffe, Socolof, Mihuc, Wagner, & Sonderegger, 2017), which creates Praat (Boersma, 2001) textgrids marking boundaries at the word and segment level. I used the English pre-trained model (originally trained on the LibriSpeech corpus) for aligning the English data. The dictionary of the model was updated with the addition of non-words used in our stimuli. No pretrained model was available for Hindi, but MFA also allows for alignment using only the data set. I used this feature to train a model on the Hindi data and align the Hindi data. The aligned text grids in both languages were spot checked for accuracy. More detailed hand adjustments were not yet done at this stage as none of these boundaries would be directly used to extract any measurements.

5.1 Analysis

Many dialects of Hindi are currently undergoing (or have undergone) a merger between the voiceless aspirated labial stop /pʰ/ and the voiceless labiodental fricative /f/, where both are produced as [f] (Dutta, 2007). All of the speakers in this study consistently produced the fricative, so I only compare the coronal and velar stops in this paper. The coronal category includes the dental and retroflex stops in Hindi and alveolar stops in English. Results do not change if the English alveolar stops are compared with only the dental stops or only the retroflex stops.

The force aligned textgrids were used as input to AutoVOT (Keshet, Sonderegger, & Knowles, 2014) which allowed for automatic measurement of lag time intervals. Prior to running AutoVOT, I extended the MFA boundaries of each long lag stop by 31ms on each side to create the intervals in which AutoVOT would measure VOT, following Chodroff (2018). I then ran AutoVOT, measuring lag time from the start of the burst to the onset of voicing. This procedure was used to measure lag time for the voiceless short and long lag stops in both languages. The intervals created by AutoVOT were all hand-checked and hand-corrected as needed by the author or a trained research assistant.

Example tokens are shown in Figures 2, 3. In both figures, the short lag tokens are on the left and the long lag tokens on the right. As expected, a difference in the duration of aspiration between the short and long lag tokens can be seen in both languages. This section focuses on lag times for the long lag stops.

Figure 2

Voiceless short and long lag stops in Hindi. Left: CV sequence from token of /tup/. Right: CV sequence from token of /tʰup/.

Figure 3

Short lag and long lag stops in English. Left: CV sequence from token of /dit/. Right: CV sequence from token of /tip/.

To abstract over differences in mean values between speakers and vowel contexts, lag time values were centered around means within-speaker, within-category, and within-vowel context. A standard outlier rejection method was applied before analysis, excluding tokens with a z-score greater than |3| (Well, Myers, & Lorch, 2010). This removed 33 of 3663 total tokens.

5.2 Results

In Figure 4, I show the distribution of lag time values for long lag stops in both languages at coronal and velar places of articulation. These plots use the centered lag time values, collapsed over speakers. Lindblom’s hypothesis predicts less within-category variation in Hindi (expected results shown in Figure 1). If this were the case, the English distributions would be wider than the Hindi distributions in the results. However, in Figure 4, the English distributions do not appear to be wider than the Hindi distributions for either place of articulation. In fact, it appears that the Hindi data might actually be slightly more variable than the English data, though this difference is insignificant.

Figure 4

Experimental results for coronal and velar long lag stops. Lag time values are centered within speaker and vowel context.

To quantify the effect of language, I use a mixed effects linear regression where within-speaker within-category lag time variance is the dependent variable. This follows Vaughn et al. (2018) who used within-category variance as a dependent variable to test for differences in group-level within-speaker variability. Variance was calculated within-speaker within-category and within vowel context (e.g., variance in speaker e-02’s productions of /t/ before /i/, etc.), over about 40 tokens in each condition. The number of tokens differs slightly across conditions because a small number of tokens were excluded, and participants occasionally skipped stimuli. The coefficient of variation was then calculated over the tokens in each condition, resulting in 90 observations of within-speaker within-category variance.

Language, Place of Articulation, and Vowel Context were also included as fixed effects with random intercepts for speaker. Although Language is the main effect of interest, other factors were included to ensure that a significant effect of language would not be due to covariation with other factors. It is possible that stop place of articulation and vowel quality may independently influence extent of variation. No random slopes for speaker were included as this additional model structure was not justified by the research question. I am interested in the main effect of language, and speaker is fully nested within language. R (R Core Team, 2013) was used for all statistical analyses. The lmer function in the lme4 package (Bates, Sarkar, Bates, & Matrix, 2007) was used for the regression model, with LmerTest to obtain p values (Kuznetsova, Brockhoff, & Christensen, 2017). Place was coded as a categorical variable with two levels: coronal and velar. Default dummy coding contrast structure was used with English coronal _/a/ context as the reference level.

Lindblom’s (1986) hypothesis predicts less group-level within-speaker variation in Hindi relative to English, therefore we would expect a significant effect of Language in the model. Under Contrast-Dependent Variation (proposed here) we do not expect this difference in group-level within-speaker variation, therefore we would expect no significant effect of Language in the model. The model output in Table 8 shows no significant effect of Language.

5.3 Interim discussion: Lag time

Despite the difference in number of stop phonemes in the two languages, the amount of group-level within-speaker lag time variability of voiceless aspirated stops is similar. This is not expected under the most direct implementation of Lindblom (1986) which predicts less variation in languages with more phonemes. Under Contrast-Dependent Variation, similar amounts of lag time variation in Hindi and English are expected, as both employ lag time as a primary cue. In the data here, there is no significant effect of Language on group-level within-category variability. As with any null effect, it could always be the case that the sample was too small to observe any significant effects. However, a significant difference in voicing variation (Section 6) was found, so this model would have detected differences of the same magnitude in lag time variation if they were present.⁶

These results can also be interpreted as providing empirical evidence for the division of lag time and lead time into separate dimensions (as in Mikuteit & Reetz, 2007), as prevoicing and lag time pattern differently. Lag time variation is similar in both languages, but (as shown in Section 6) closure voicing variation differs between Hindi and English. Analyzing prevoicing and lag time as separate phonetic dimensions captures these differences.

6.1 Analysis

Closure duration and closure voicing were hand measured for all stops. Fifty-seven tokens with stop closures longer than 300 ms were excluded. Outlier rejection of tokens with a z-score greater than |3| also excluded 47 tokens. Closure duration was measured from the offset of the preceding vowel until the stop burst. Vowel offset was determined by lack of all formant structure except the lowest formant, following Turk, Nakai, and Sugahara (2006). Closure voicing was measured as the portion of the stop closure which contained periodicity in the waveform, indicating voicing. The percentage of the closure containing voicing was calculated from the measurements of closure duration and closure voicing. Operationalizing voicing with a percentage measurement follows previous work on voicing in English (e.g., Docherty, 1992; Davidson, 2016). These data were also classified according to three categorical bins: no prevoicing (voicing through 0–25% of the stop closure), partial prevoicing (25–90%), and full prevoicing (90–100%). The classification of full prevoicing as voicing through 90% or more of the closure duration follows the categorization in Beckman et al. (2013).

For tokens that are only partially voiced, the percentage measurement does not convey the shape of that voicing, or where in the closure the voicing is present. Davidson (2016) distinguishes multiple shapes of partial voicing for obstruents. ‘Bleed’ describes voicing that continues from the preceding segment but dissipates some time during closure, before the stop burst. ‘Trough’ describes voicing that continues from the preceding segment, dissipates, and then reappears before the stop burst. ‘Hump’ describes cases where voicing does not continue from the preceding segment, then appears in some middle interval of the closure, and then dissipates again before the burst. Lastly, ‘Negative VOT’ describes voicing that starts in the middle of the stop closure and continues into the burst. In this paper, I only analyze cases of bleed, the pattern displayed in almost all partially prevoiced stops in both languages. Some cases of negative VOT, trough, and hump were observed, but most fell into the group of tokens excluded on the basis of long closure durations (> 300 ms). Thirty additional cases of trough were also excluded, all with closures in the 250–300 ms range (just missing the criteria for exclusion on the basis of closure duration). This was done because the percentage measurement does not capture the differences between bleed and trough, and there were not enough trough tokens to analyze shape differences systematically. After all exclusions, 3512 tokens were available for voicing analysis.

Example tokens are shown in Figures 5, 6. The Hindi tokens in Figure 5 show voicing before the stop closure which continues through the burst into the vowel. Phonetically voiced and voiceless realizations of the English phonologically voiced stops were observed. The English token in Figure 6 differs from the other phonologically voiced English example token shown in Figure 3. Voicing starts before the stop burst in Figure 6, but after the stop burst in Figure 3.

Figure 5

Voiced unaspirated and aspirated stops in Hindi. Left: CV sequence from token of /dut/. Right: CV sequence from token of /dʰup/.

Figure 6

Short lag stop with closure voicing in English.

6.2 Results: Extent of voicing variation

In this section, I analyze extent of within-category variation of phonologically voiced stops in both languages. Because this analysis is comparative and there are no voiced aspirated stops in English, the Hindi voiced aspirated stops have been excluded from the analysis. The pattern of results does not change (there is still more variation in English relative to Hindi) if the voiced aspirated stops in Hindi are included. Figure 7 provides a density plot of the closure voicing percentages in both languages, collapsed over speaker and vowel context. In Hindi, the distribution of proportion voiced is skewed as almost all stops are produced with voicing during 100% of the closure. In English, the distribution of voicing is more variable.

Figure 7

Density plot of percentage of voicing during stop closures.

In Figure 8, I show the same data binned according to voicing category (no prevoicing, partial prevoicing, full prevoicing). Error bars show standard deviation between speakers. In Hindi, almost all voiced stops are produced with full prevoicing (voicing through at least 90% of closure duration). In English, there is more overall variation in degree of prevoicing. Most of the phonologically voiced stops produced in English are partially prevoiced but this varies across speakers.

Figure 8

Voicing during stop closure in phonologically voiced stops (categorical bins); Error bars show standard deviation between speakers.

As in the lag time analysis, I use mixed effects linear regression where within-category variance is the dependent variable. This was calculated by determining the variance in closure voicing within stop category, speaker, and vowel context, which was then used to calculate the coefficient of variation for each condition. This resulted in 126 observations of variance. Language, Place, and Vowel were again included as predictors with random intercepts for speaker. Default dummy coding contrast structure was used with the English coronal _/a/ context as the reference level. The model output is given in Table 9. Under Contrast-Dependent Variation, we expect less group-level within-speaker variation in Hindi relative to English. We therefore expect a significant effect of Language in the model, which was observed. The effect size of Language is large (d = 2.01), which is expected given the substantial body of work documenting voicing variation in English.

6.3 Results: Structure in voicing variation

6.3.1 Individual differences

There are some individual differences in extent of closure voicing variability in Hindi, but all Hindi speakers consistently fully voice the majority of phonologically voiced stops. Figure 9 shows distributions for the two Hindi speakers with the most between-speaker difference in amount of voicing. I also provide the data binned in discrete voicing categories for all speakers in Figure 10.

Figure 9

Hindi speakers with greatest difference in voicing.

Figure 10

All Hindi speakers in order of amount of voicing.

English speakers, however, do not display a consistent pattern in degree of closure voicing. Some English speakers exhibit closure voicing on almost all phonologically voiced stops while others exhibit little closure voicing. Figure 11 shows the two English speakers with the most between-speaker difference in closure voicing. These two English speakers display near opposite patterns. Figure 12 provides the same data binned into voicing categories, along with the data from all other English speakers. The English speaker with the most voicing exhibits a pattern which resembles that of the Hindi speakers—the majority of phonologically voiced stops exhibit full prevoicing. The English speaker with the least voicing exhibits the opposite pattern, with about 35% of stops showing no closure voicing and more than half of stops showing partial voicing. These graphs also demonstrate the fact that the ‘average’ pattern (Figure 8) is not particularly representative of the individual English speakers. By contrast, multiple Hindi speakers mirror the ‘average’ pattern for Hindi.

Figure 11

English speakers with greatest difference in voicing.

Figure 12

All English speakers in order of amount of voicing.

6.3.2 Variation across vowel contexts

Smith and Westbury (1975) report more prevoicing in English stops before high vowels relative to low vowels. I observe a similar pattern in the English data, but not in Hindi. Just as the pattern of voicing in Hindi is fairly consistent across speakers, the pattern of voicing is also consistent across vowel contexts. The data for both languages are shown in Figures 13, 14.

Figure 13

Prevoicing across vowel contexts in Hindi phonologically voiced stops.

Figure 14

Prevoicing across vowel contexts in English phonologically voiced stops.

6.3.3 Modeling sources of variance

To illustrate the differences in sources and structure of voicing variability, I compare the effects of different factors in accounting for overall voicing variance in both languages. In these models, percent of closure with voicing is the dependent variable, rather than variance in closure voicing as in Table 9. Due to the dependent variable being continuous proportion data, I use Beta Regression (Ferrari & Cribari-Neto, 2004), which is intended for proportion data bounded between (0,1). Unlike a standard linear regression which assumes the data follow Gaussian distributions, the Beta Regression assumes Beta distributions, which tend to be more characteristic of proportion data. As evident from the density plots in the previous section, the proportion data here are not normally distributed and are better approximated with Beta distributions.

Separate regression models were fit for English and Hindi using the following factors as predictors: phonological voicing, place of articulation, speaker, vowel context (V), experimental block, and closure duration, with random intercepts for item. The following interactions were also included in the full models: place × V, place × speaker, and V × speaker. These models included all of the stops elicited, both phonologically voiced and voiceless. Models were fit using the betareg (Cribari-Neto & Zeileis, 2010) and glmmTMB (Brooks et al., 2017) R packages. Best fit models for both languages were determined using variable selection with the Akaike Information Criterion (AIC; Akaike, 1974). Likelihood ratio tests were performed using the lmtest package (Zeileis & Hothorn, 2002) and stepwise selection was performed using the MASS package (Ripley et al., 2013).

While using fixed effects to model factors like vowel context or place of articulation is typical, speaker effects are often modeled using random effects (Allen et al., 2003; Baayen, Davidson, & Bates, 2008). However, the main question for these models is how sources of variance differ in the two languages. This is different from the previous models shown in Tables 8, 9. The question under investigation there was whether extent of variation differed between languages, for which I did include speaker as a random effect. Including speaker as a fixed effect allows for quantitative measurement (via the R squared value) of how much variation is accounted for by Speaker relative to the other factors. In the present analysis, significance of main effects is not the focus. Instead, the main question is how much variance is accounted for by each factor and how the best fit models differ between languages. If speaker is included as a random effect, the overall pattern of results about extent of variability remains consistent—there is more voicing variation in English relative to Hindi. The fixed effect analysis allows us to gain more insight into how sources of variation differ between the two languages.

In the full models, there is a significant effect of phonological voicing in both languages, indicating more closure voicing for phonologically voiced stops relative to phonologically voiceless stops. In English, there is a also significant effect of the high vowel /i/ (indicating more voicing relative to /a/), but neither of the vowel effects are significant in Hindi. Many speaker effects are significant in English, while there there are no significant speaker effects in Hindi. In addition, there are several significant interactions between speaker, vowel context, and place of articulation in English, while none of the interactions reach significance in Hindi.

The differences between the full models for the two languages result in different best fit models using the AIC criterion for model selection. The best fit model for the Hindi data (given in Table 10) includes only two of the predictors from the full model: phonological voicing and closure duration. With these two factors, this model accounts for 78% of the overall voicing variation in the data. Vowel context, speaker, block, their interactions, or the random effect of item do not significantly improve the model fit, which indicates that they are not significant sources of voicing variation in Hindi. A likelihood ratio test comparing the full model to the best fit model verifies this (given in Table 11). The non-significant Chi Square value indicates that there is no significant change in log likelihood when the full model is reduced to the best fit model.⁷

Table 10

Effect table for best fit model in Hindi. Beta regression with logit link. Dependent variable: closure voicing. Call: voicing percent ∼ phonological voicing + closure duration.

Effects	Estimate (SE)	z	p
(Intercept)	3.69 (0.09)	43.23	<0.001***
Phonological voicing	–4.92 (0.09)	–55.58	<0.001***
Closure duration	–2.26 (0.45)	–5.05	<0.001***
Pseudo R2: 0.78

Table 11

Model comparison: Likelihood ratio test of Hindi restricted model versus full model.

Model	#Df	Log Likelihood	Change in Df	ChiSq	p
1	4	6221.10	–37	36.82	0.48
2	41	6239.60

• Model 1: best fit model (voicing percent ~ phonological voicing + closure duration).

  Model 2: full model (voicing percent ~ voicing + V × speaker + place × V + place:speaker + closure duration + block + (1 | item)).

The best fit model for the English data in Table 12 includes the same predictors as the best fit model in Hindi (voicing and closure duration) as well as vowel context, speaker, the V × speaker interaction, the place × V interaction, the place × speaker interaction, and experimental block. This model accounts for 40% of the overall closure voicing variation in the English data. The only factor from the full model which is not included in the best fit model is the random effect of item. However, including that effect does significantly improve model fit, as is seen in the significant Chi Square value in a likelihood ratio test comparing the English best fit model to the English full model (Table 13).

Table 12

Main effect table for best fit model in English. Beta regression with logit link. Dependent variable: closure voicing. Call: voicing + V × speaker + place × V + place:speaker + closure duration + block. Intercept is speaker e02 voiced coronal /a/ context block 1.

Effects	Estimate (SE)	z	p
(Intercept)	2.41(0.23)	10.34	<0.001***
voicing-voiceless	–1.54 (0.07)	–23.28	<0.001***
V-/i/	0.66 (0.20)	3.24	0.001**
V-/u/	–0.40 (0.21)	–1.86	0.063
speaker-e03	0.55 (0.23)	2.40	0.016*
speaker-e04	–0.66 (0.23)	–2.86	0.004**
speaker-e06	–1.04 (0.23)	–4.56	<0.001***
speaker-e07	–1.94 (0.23)	–8.37	<0.001***
speaker-e09	–1.90 (0.23)	–8.30	<0.001***
place-labial	–0.48 (0.20)	–2.35	0.019*
place-velar	–0.21 (0.20)	–1.04	0.299
closure duration	–9.99 (1.02)	–9.77	<0.001***
block 2	0.05 (0.08)	0.62	0.535
block 3	–0.05 (0.08)	–0.64	0.525
block 4	0.05 (0.08)	0.61	0.541
V-/i/:speaker-e03	–0.44(0.25)	–1.79	0.073
V-/u/:speaker-e03	0.83(0.25)	3.31	<0.001***
V-/i/:speaker-e04	–0.07(0.25)	–0.27	0.788
V-/u/:speaker-e04	1.37(0.26)	5.30	<0.001***
V-/i/:speaker-e06	–0.55(0.25)	–2.20	0.028*
V-/u/:speaker-e06	0.50(0.26)	1.94	0.053
V-/i/:speaker-e07	0.07(0.25)	0.29	0.774
V-/u/:speaker-e07	1.00(0.26)	3.89	<0.001***
V-/i/:speaker-e09	–0.11(0.25)	–0.45	0.656
V-/u/:speaker-e09	0.84(0.26)	3.28	0.001**
V-/i/:place-labial	–0.08(0.17)	–0.49	0.622
V-/u/:place-labial	0.08(0.19)	0.41	0.682
V-/i/:place-velar	0.24(0.18)	1.35	0.177
V-/u/:place-velar	0.24(0.18)	1.34	0.182
speaker-e03:place-labial	0.50(0.25)	2.01	0.045*
speaker-e04:place-labial	0.11(0.26)	0.44	0.660
speaker-e06:place-labial	0.37(0.25)	1.45	0.146
speaker-e07:place-labial	0.70(0.25)	2.77	0.006**
speaker-e09:place-labial	0.51(0.25)	2.01	0.044
speaker-e03:place-velar	–0.24(0.25)	–0.95	0.342
speaker-e04:place-velar	–0.68(0.26)	–2.64	0.008**
speaker-e06:place-velar	–0.50(0.26)	–1.94	0.053*
speaker-e07:place-velar	0.02(0.25)	0.09	0.931
speaker-e09:place-velar	–0.11 (0.25)	–0.42	0.674
Pseudo R2: 0.40

Table 13

Model comparison: Likelihood ratio test of English restricted model versus full model.

Model	#Df	Log Likelihood	Change in Df	ChiSq	p
1	40	1954.0	–1	8.87	0.003**
2	41	1958.40

• Model 1: best fit model (voicing percent ~ voicing + V × speaker + place × V + place:speaker + closure duration + block).

  Model 2: full model (voicing percent ~ voicing + V × speaker + place × V + place:speaker + closure duration + block + (1 | item)).

The differences in the best fit models and likelihood ratio tests between the two languages show how the sources of stop voicing variation differ between Hindi and English. The best fit model in Hindi (with only voicing and closure duration as predictors) accounts for 78% of the overall voicing variation. However, the best fit model in English (with almost all predictors included) only accounts for 40% of the overall voicing variation. The variance accounted for by individual predictors also differs between the two languages. In the graphs in Figure 15, I show the proportion of total variance accounted for by each individual factor in the full models for both languages. In the Hindi model, almost 78% of the overall voicing variation is accounted for by phonological voicing. All other factors account for less than 1% of overall voicing variation in Hindi. In the English model, only 14% of the overall voicing variation is accounted for by phonological voicing while around 22% of the overall voicing variation is accounted for by speaker.

Figure 15

Proportion of total variance accounted for in regression models.

Although the full model for English still only accounts for 40% of the overall variance, this does not necessarily indicate that the remaining 60% percent of the variance is due to random variation. It could be the case that this variation is also structured by additional factors which are not analyzed in these models. What can be concluded from these models is that (1) the factors analyzed here account for less of the overall variance in the English data relative to the Hindi data, and (2) the strongest predictor of amount of closure voicing in Hindi is phonological voicing while the strongest predictor of voicing in English is individual speaker.

6.4 Interim discussion: Voicing

This section has examined extent and structure of closure voicing variation in Hindi and English stops. The pattern of voicing in Hindi is largely consistent across speakers and vowel contexts. The English speakers vary more in closure voicing both within- and between-speakers. Sources and structure of voicing variation also differ between the two languages. While phonological voicing accounts for ∼78% of the total voicing variation in Hindi, it accounts for only 14% of the total voicing variation in English. Speaker, vowel context, and their interactions significantly contribute to the English model, showing that the additional variation in English is structured according to these non-contrastive factors. However, these factors together still account for only 40% of the overall voicing variation in the English data. This suggests that there is either more random variation in English voicing relative to Hindi, or there are additional factors that structure the English variation which are not considered here. The following subsections discuss these voicing results in light of the literature on prevoicing in English stops (Section 6.4.1), review implications for laryngeal realism and English featural analyses (Section 6.4.2), and review a potential articulatory explanation for voicing differences across vowel contexts (Section 6.4.3).

7.1 Inventory size and phonetic variability

Lindblom’s (1986) hypothesis “that phonetic values of vowel phonemes should exhibit less variation in small systems than in large systems” is intuitive, but has scant and conflicting evidence in the literature. Previous work has shown it to be inadequate for predicting patterns of variability in vowel inventories, demonstrating the need for a more nuanced approach (see Section 2.3.2 for a review). Similarly, the results here show that it is not the case that phonetic values in larger ‘systems’ are always less variable—Hindi speakers showed just as much variation as English speakers in voiceless lag time, despite having twice the number of stop phonemes.

In this paper, I have proposed an alternative hypothesis which makes predictions about phonetic variability according to cue weight of individual phonetic dimensions rather than size of phonemic inventories. This accounts for the differential behavior of individual cues with respect to variability patterns (as demonstrated here and in e.g., Recasens & Espinosa, 2006 for vowels) as well as importance of phonological context, as demonstrated by Renwick (2012). Contrast-Dependent Variation incorporates these notions by making separate predictions about individual phonetic dimensions through the use of cue weight, which differs across phonological contexts. For example, the predictions of Contrast-Dependent Variation would change for Hindi versus English stops in syllable-final position (as opposed to utterance-medial word-initial position, examined here), as cue status and relative weights differ across positions. As the predictions are tied to cue weights in particular contexts, this hypothesis only makes predictions about token-by-token variability and is not intended to account for variation across phonological contexts or contextual allophonic variation. Testing the generalizability of Contrast-Dependent Variation by directly examining extent of variation across contexts is an area for future work.

While Contrast-Dependent Variation offers an alternative to DT predictions, it is in some ways consistent with the original intuition behind Lindblom (1986). Lindblom’s hypothesis (and work in DT more generally) assumes that production is optimized for ease of perception through sufficient dispersion of phonological categories in acoustic space. Contrast-Dependent Variation still carries an implicit assumption about the relevance of perceptual distinction by incorporating cue weight and assuming that speech sound contrasts must somehow be sufficiently distinct. However, by comparing cue weights of individual dimensions rather than inventory size, this approach acknowledges the multidimensional context-sensitive nature of phonological contrast. Therefore, no differences in patterns of variability are predicted based solely on phoneme inventory size. Rather, for a single phonetic dimension, we expect less within-category variability in languages for which that dimension is employed as a primary cue relative to languages in which that dimension is employed as a secondary cue.

7.2 Perception and cue weighting

Modeling work on cue weighting has shown that algorithmically weighting cues based on how reliably they distinguish phonological contrasts mirrors the cue weighting patterns observed in perceptual data (Toscano & McMurray, 2010). The model employed by Toscano and McMurray (2010: 438) estimates the reliability of a phonetic dimension with a ratio of mean values to within-category variances, mirroring the Dispersion Theoretic assumption that distributions must be tightened in a crowded space to avoid overlap and preserve perceptual distinction. This type of model is supported by empirical work on the relationship between within-category variability and cue weighting in perception. For example, Clayards et al. (2008) show that perceptual uncertainty increases with within-category phonetic variability and Holt and Lotto (2006) show that cue weighting strategies are affected by changes in input variability.

The present results provide empirical support from production for the inclusion of within-category variance in cue-weighting models. A prediction that arises from the reliability definition in Toscano and McMurray (2010) is that strength of cue and relative amount of within-category variation should be inversely correlated, which is supported here through between-language and within-language comparison. English speakers exhibited more variation in extent of closure voicing (a secondary perceptual cue) relative to Hindi speakers, for whom voicing provides a primary perceptual cue. Within English, speakers also display less variation in the primary cue of lag time relative to the secondary cue of voicing.