Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Dispersion (and lack thereof) in consonantinventories

Ivy Hauser

2nd Year ConferenceMay 4, 2015

0

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Introduction

Dispersion Theory (DT) in vowel inventories is robust(Liljencrantz and Lindblom 1972, Schwartz et al. 1997).General idea: vowel inventories tend towardsconfigurations which maximize perceptual distinctiveness.

1

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Introduction

Dispersion Theory (DT) in vowel inventories is robust(Liljencrantz and Lindblom 1972, Schwartz et al. 1997).General idea: vowel inventories tend towardsconfigurations which maximize perceptual distinctiveness.

1

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Introduction

Dispersion Theory (DT) in vowel inventories is robust(Liljencrantz and Lindblom 1972, Schwartz et al. 1997).General idea: vowel inventories tend towardsconfigurations which maximize perceptual distinctiveness.

1

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Introduction

Dispersion Theory (DT) in vowel inventories is robust(Liljencrantz and Lindblom 1972, Schwartz et al. 1997).General idea: vowel inventories tend towardsconfigurations which maximize perceptual distinctiveness.

1

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Are consonants inventories also dispersed?

Preliminary: What is dispersion?Any discussion of dispersion assumes a phonetic space.For vowels: first and second formants.

2

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Are consonants inventories also dispersed?

Preliminary: What is dispersion?Any discussion of dispersion assumes a phonetic space.For vowels: first and second formants.

Spectrogram showing frequency over time.Darkness = intensity

Formants change with articulatory changes:

2

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Are consonants inventories also dispersed?

What is the right phonetic space for consonants?More perceptual cues available.Different types of consonants might behave differently.

This project: Given some phonetic space for consonants -How should dispersion be measured?Do we observe the predictions of DT?

3

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Analysis of two spaces

Part 1: Stop place of articulation (POA)Schwartz et al. (2012): dispersion predicts [d-g-P] as theoptimal inventory.Does this hold up under a better metric for measuringdispersion?

Part 2: Stop voicing (VOT)DT prediction: bigger inventory = larger phonetic space,less variationDoes this hold with consonants?Test case with voicing contrasts in Hindi (4 contrasts perPOA) and English (2 contrasts per POA)

4

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Part 1: Background

Dispersion prediction: Common inventories should bedispersed in the appropriate space.

Most common three stop inventory: [labial-coronal-velar]([b-d-g] [p-t-k])Schwartz et al. (2012): Vocal tract model data shows[coronal-velar-pharyngeal] is the most dispersed.Does this claim hold up under a better metric fordispersion?

This project: Re-analysis of vocal tract model data andproposal of new dispersion metric.

5

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

The vocal tract model

Data from vocal tract model based on Maeda (1990), usedby Schwartz et al. (2012).50,000 stop tokens generated along the vocal tract andclassified according to POA.Formants measured at onset of following vowel, firstappearance of formant structure after release.

Formant transitions from /d/

6

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Formant transitions as a space for stops

Are formant transitions into the following vowel a reasonablespace?

Yes - experimental evidence shows formants are primarycues.Walley and Carrell (1983): Adults and children use formanttransitions for identification when burst and transitionsconflict.Sussman et al. (1991): F2 transitions between consonantand vowel effectively categorize stops by place (locusequations).

7

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

The data: F1/F2 space

F1/F2 (kHz): lots of overlap, F1 difference for epi-pharyngeals only8

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

The data: F2/F3 space

F2/F3 (kHz): lots of overlap, categories different shapes and sizes9

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

What categories are dispersed in this space?

Claim: [coronal-velar-pharyngeal] is the most dispersedconfiguration. (Schwartz et al., 2012)

when

Dispersion = the mean-to-mean distance between categories.

Conclusion is entirely dependent on dispersion metric.

10

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Mean to mean dispersion

POA1 POA2 POA3 Dispersion (area)1 coronal epi-pharyngeal velar 0.42 coronal uvular velar 0.33 epi-pharyngeal palatal velar 0.294 bilabial coronal epi-pharyngeal 0.235 bilabial coronal velar 0.236 coronal epi-pharyngeal uvular 0.217 bilabial epi-pharyngeal palatal 0.28 coronal epi-pharyngeal palatal 0.29 epi-pharyngeal palatal uvular 0.2

10 coronal palatal uvular 0.1911 palatal uvular velar 0.1912 bilabial coronal palatal 0.1613 bilabial palatal velar 0.1614 bilabial epi-pharyngeal velar 0.115 epi-pharyngeal uvular velar 0.116 coronal palatal velar 0.08717 bilabial palatal uvular 0.08518 bilabial coronal uvular 0.08219 bilabial uvular velar 0.06920 bilabial epi-pharyngeal uvular 0.043

11

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

F2/F3 space and mean to mean distance

12

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

F2/F3 space and mean to mean distance

13

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

The problem with mean-to-mean distance

Intuitively not equal to dispersion:Two distributions - same means but different variances.

We want the one on the left to be better dispersed.

14

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Incorporating distribution shape into dispersion

Within category variance:Should have some effect on perception.Should be considered in a dispersion metric.

Jeffries-Matusita DistanceNot measuring between two points (mean to mean) butbetween two distributions.Incorporates covariance of the category distribution - themultidimensional analog of variance.A true distance metric (metric axioms: symmetry, triangleinequality)

15

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Jeffries-Matusita distance

POA1 POA2 POA3 Mean JMdist Area1 bilabial coronal epi-pharyngeal 1.413 0.864542 bilabial palatal uvular 1.4124 0.863793 bilabial epi-pharyngeal palatal 1.4124 0.86384 bilabial coronal uvular 1.4111 0.862175 epi-pharyngeal palatal uvular 1.4058 0.855526 bilabial epi-pharyngeal uvular 1.4057 0.855467 epi-pharyngeal uvular velar 1.4056 0.855348 coronal epi-pharyngeal uvular 1.4038 0.853199 coronal epi-pharyngeal velar 1.4024 0.85131

10 coronal uvular velar 1.4004 0.8488411 bilabial epi-pharyngeal velar 1.3555 0.7871312 bilabial uvular velar 1.3553 0.7869713 coronal epi-pharyngeal palatal 1.3535 0.7842314 coronal palatal uvular 1.3516 0.7822415 bilabial coronal palatal 1.3505 0.7811816 bilabial coronal velar 1.3426 0.773517 epi-pharyngeal palatal velar 1.3365 0.7589118 palatal uvular velar 1.3364 0.7587719 bilabial palatal velar 1.2761 0.6921720 coronal palatal velar 1.2641 0.6823 16

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Part 1 Conclusions

Dispersion does not make the correct predictions for typologicalfrequency in stops.

The [labial-coronal-velar] inventory will not be optimal inthis space even with a better metric.Pharyngeals and epiglottals - problematic places fordispersion because of F1 differences.Dispersion is not typologically predictive for consonants inthis space, cannot solely explain prevalence of [b-d-g].

17

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Part 2: VOT space

Voice onset time durations as a different space for dispersion inconsonants.

DT prediction: Overall phonetic space size shouldincrease with number of segments, within categoryvariation should decrease (Liljencrantz and Lindblom,1972).Do these predictions extend to consonants?Test case: VOT in Hindi (4 contrasts per POA) andEnglish (2 contrasts per POA) as a test case.

18

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

The experiment

Participants: Two speakers each of Hindi and AmericanEnglish. All the speakers were female and between ages 20-35.

Stimuli: CVC words and non-words with vowels [i a u], recordedin carrier phrases (“Say X again” in English).

Goal for stimuli to be as similar as possible betweenlanguages.Words and non-words used for Hindi and English.English stimuli grouped further according to lexicalstatistics.

19

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Segments under consideration

Labial Dental/Alveolar Retroflex VelarHindi p ph b bh t th d dh ú úh ã ãh k kh g gh

English p b t d k g

Four contrasts per place of ariculation in Hindi.Two contrasts per place of articulation in English.

20

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Analysis

Voiceless stops: positive VOT measured from the burst to theonset of voicing.

21

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Analysis

Prevoiced stops: negative VOT measured from the start of theocclusion to the burst.

22

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

English results

Speakers: No significant differences in VOTs for any segmentsin any vowel contexts.Data collapsed over speakers.

VOTs in / i/ context: 2 distinct modes, other contexts similar

23

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Hindi results

Speakers: Differences in VOTs /p/, /bh/, /t/, /dh/, /k/.Data collapsed over speakers.

VOTs in / a/ context: 3 distinct modes, other contexts similar

24

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Results: Hindi vs. English

VOTs in Hindi and English collapsed over contexts and speakers:3 modes in Hindi, 2 modes in English.Space doesn’t appear bigger in Hindi.

25

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Results: Hindi vs. English

VOT distributions: Hindi space doesn’t look bigger.English boxes do look bigger - more variation.

26

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Within category variance

Place English VOT (ms) Hindi VOT (ms) Levene’s Test pLabial-vcls 75.53 ± 21.52 85.11 ± 9.67 6.15e-05 ***Labial-vcd -105.1 ± 16.8 -91.56 ± 14.38 0.02 *

Coronal-vcls 93.45 ± 20.3 86.38 ± 12.25 0.002 **Coronal-vcd -101.3 ± 21.23 -89 ± 17.13 0.04 *Velar-vcls 100.8 ± 19.4 97.4 ± 18.6 0.9Velar-vcd -90 ± 17.2 -72 ± 16.4 0.7

Variance within VOT categoriesLevene’s Test: whether variances are equalEnglish categories have more variation except for velars.Variation prediction of DT: held up in VOT space for labialsand coronals.

27

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Conclusions: VOT and dispersion predictions

Space-size prediction: not held up.→ But this is very context dependent.

In isolation English would not have negative VOTs and theHindi space would be bigger.Context is part of the phonetic space.

Variation prediction: holds for all segments except velars.Within category variances of English VOTs categories arebigger than Hindi.

28

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Overall conclusions

Does dispersion theory apply to consonant inventories?

As a predictive typological model: NoDispersion doesn’t predict typological trends.Possible options: consonants are not dispersed, or wedon’t have the right phonetic space.

But perceptual distinctiveness effects are real inconsonants.

VOT variance data in Hindi and English give evidence forthis.Perceptual distinctiveness without the typologicalframework of dispersion theory.

29

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Acknowledgements

Thanks to John Kingston, Kristine Yu, Sang-Im Lee Kim, LynFrazier, Sound Workshop and PRG audiences for advising andinput.Thanks to Sang-Im Lee Kim, Yangsook Park, Sakshi Bhatia,Jyoti Iyer, Coral Hughto, and Megan Somerday for voices.

This material is based upon work supported by the NationalScience Foundation Graduate Research Fellowship Programunder Grant Number 2014175439. Any opinions, findings, andconclusions or recommendations expressed in this material arethose of the author(s) and do not necessarily reflect the viewsof the National Science Foundation.

30

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Stop system data

All data from the 628 phonological inventories of P-base.

Assumptions:d stands for both alveolar and dental place.Retroflexes not included because they are usuallycontrastive.

Languagesb d g 429b d - 35b - g 2- d g 5none 133

31

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Stop system data

Languagesp t k 545p t - 3p - k 0- t k 59

none 15

Languagesm n N 327m n - 276m - N 1- n N 0none 10

32

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Triangle areas

A geometric problem:

We want equilateral - Incorporating variance of sides.

33

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

English results by word type

34

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Example stimuli

Example Stimuli:Language C1 vowel stimulus (IPA) type

Hindi b i bit wordHindi kh i khil wordHindi bh u bhut wordHindi d a dag word

English p i pis word-hiEnglish t u tun word-lowEnglish t i tiT word-hiEnglish b a bag word-low

35

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

VOT means: Hindi vs. EnglishEnglish

segment VOT- -ph 74.07- -th 92.23- -- -- -kh 98.99b -105.05- -d -101.25- -- -- -g -91.35- -

Hindi

segment VOTp 10.08ph 83.86t 12.47th 86.58T 11.25Th 82.11k 39.97kh 100.00b -99.03bh -83.69d -98.44dh -79.91D -95.75Dh -80.08g -77.83gh -80.00

36

Introduction Part 1: Stop Place of Articulation Part 2: Voice Onset Time Conclusions Appendix

Covariance

covx,y =∑N

i=1(xi−x)(yi−y)N−1

37