10/12/16

1

Interaction of high vowel devoicing and

syllabificationNINJAL,13October, 2016

Syllables andProsody Workshop

Shigeto Kawahara& JasonShawKeioUniversityandYaleUniversity

Introduction

What wewouldliketodo

• Japanesehighvowelsare“devoiced”insomeenvironments.

• Q1:Arethesevowelsdeleted?Oraretheydevoiced?

• Q2:Iftheyaredeleted,thenwhat’stheconsequenceformoraification/syllabification?

• (Q3:ifJapanesespeakershaveCVCsyllablesduetohighvoweldeletion,aretheysensitivetosyllable-relatedphonotacticrestrictions,possiblyprovidedbyUG:cf.Berent’s recentwork?)

• Weoffernewarticulatorydataonthetabletobearonthesequestions.

Japanese highvoweldevoicing

• The standard description: Highvowels aredevoicedbetween twovoiceless consonants.

• Anexample, [ʃutaisei] “willingness”.

• “Devoicing” is thetraditional term used. However,whether these vowelsaresimply devoiced ordeleted stillremain debated.

• Devoiced=oral gestures remain intactorreduced.• Deleted=oral gestures deleted.

Devoicingvs.Deletion?

• Aphonetic devoicing view:• Laryngeal gestures overlap to passively devoicevowels (e.g., Jun & Beckman 1993).

• Aphonological deletion view:• Và ∅ /[-voice] _____ [-voice] (Kondo 2000)• Or aphonological process induced by asetof

markedness constraints (Tsuchida 1997).

Laryngealtier:

Oraltier:

devoicing devoicing

ʃ u t

Orcoulditbeboth?

• Kawakami(1971)listsenvironmentswherethetargetvowelsaredeletedandwheretheyaredevoiced.

• ButKawakamigivesnoinstrumentalevidence.

• Whang(2014,2016)arguesthatvowelsaredeletedwhentheiridentitycanberecoveredfromtheprecedingconsonants.

• Forexample,thedevoicedvowelispredictably[u]after[ɸ], butitisnotafter[k].Onlyintheformerenvironmentcanthevowelbedeleted.

10/12/16

2

Japanese laryngeal gestures

• In devoiced vowelcontexts, e.g., /kite/, thereisasingle laryngeal gestureof greatermagnitudethan asingle consonant gesture, c.f. /kide/(Fujimoto etal.2002)

Howaboutlingualgestures?

• However,thelingualgestureofdevoicedvowelsisunderstudied.

• Funatsu &Fujimoto(2011),asfarasweknow,istheonlyexception.TheyusedEMMA,EGG,andPGG.

• TheirEMMAresultsshowonlysmalldifferencesbetweenvoiced[i]anddevoiced[i](implying“devoicing”,not“deletion”).

• Buttheystudiedonlyonespeaker.Andonlyonepairofstimuli([kite]vs.[kide]),oneofwhosememberisarealword. 4repetitionsandnoquantitativeanalyses.

Summary ofpastwork

• ThelaryngealdataindicatethatdevoicinginJapaneseisactivelycontrolled.

• Acousticevidenceforpresence/absenceofalingualvowelarticulationhasbeenlargelyequivocal,withsomestudiesclaimingthevowelhasbeendeleted,whileothersclaimingthatitispresent.

• Deletion:Beckman1982;Beckman&Shoji1984;Kondo1997,2000.

• Devoicing: Jun&Beckman1993;Faber&Vance2001;McCawley1968.

• Bothpossible:Kawakami1971;Tsuchida 1997;Whang2014

Ourcontributiontothedebate

• Electromagnetic Articulography (EMA) data onthelingualarticulation ofvowelsinvoicedanddevoicedcontexts.

• Acomputational method forevaluating voweldeletion onthe basisofmovementkinematics(Shaw &Kawahara,submitted).

• Showingthat somecasesdoinvolvecasesofactualdeletion.

• SomeimplicationsforprosodicorganizationofJapanese(stillinprogress).

EMAExperiment

EMA experiment

• Record the articulatory dynamics of voiced vowelsand devoiced vowel counterparts.

• Apply Discrete Cosine Transform tofit theobserveddata.

• Use aBayesian classifier toevaluatethe likelihoodthatdevoiced tokens have alingual articulation.

10/12/16

3

Materials (target words)Devoicing/deletion Voiced vowel

Entropy = 1.99 (W,K) masutaaマスター masuda益田

Entropy = 1.89 (w) hakusai 白菜 yakuzai 薬剤

Entropy = 1.46 ʃutaisei 主体性 ʃudaika主題歌

Entropy = 1.08 (W,K) ɸusoku 不足 ɸuzoku 付属

Entropy = 0.09 (W,K) katsutoki 勝つ時 katsudou 活動

10-15repetitions of thetarget wordsinthe carrier phrase: okee ______ toitte ‘Ok,say ______’. Participants were instructed tospeak as if they were making a requestof a friend. Interspersed with10wordslacking /u/

ApparatusMagnetic Field

Generatorsensorwires(toSCU)

sensor

shotgunmic

Stimulusdisplaymonitor

Sensor placement

Tongue Tip(TT) 1cm behind the tip

Tongue Blade(TB) half-waybetween the TD and TT sensors

Tongue Dorsum(TD) as farbackas comfortable forparticipant

Jaw sensor Additional sensors:Upper lip (UL)Lower Lip (LL)JawNasianLeft/Right mastoids

Lingualsensors

Procedure• Stimuliwerepresentedonamonitorinrandomorder.• Topromotefluentreading,targetwordswerepreviewedbeforedisplayedinthecarriersentence.

• AnativespeakerofJapanesemonitoredpronunciationmanuallyadvancingtrialsafteracceptingorrejectingeachtoken.

主体性 オーケー主体性と言って。

Target preview (500ms) Target sentence

Post-processing

• Headmovements corrected computationally• Datarotated totheocclusal (bite) plane

• Robust smoothing (Garcia 2010)

EMASensors

Acousticresults: voweldevoicingInlinewithcurrentdescriptionsofTokyo Japanese, /u/wasdevoiced between voicelessconsonantsandvoicedotherwise.

ɸu?s oku ɸuz o ku

/ɸusoku/ /ɸuzoku/

10/12/16

4

Results: ɸusoku~ɸuzoku

e /u/

s/z

TD islowerfordevoicedvowel

VerticalPo

sition(m

m) o

TT riseisearlierfor/s/ than /z/

Time (ms)

S01

Tip

Blade

Dorsum

Red=devoicedBlue=voiced

Results: ʃutaisei~ʃudaika

e

a

/u/

ʃ

t/d

LowerTD andTBfor devoiecd

/u/

VerticalPo

sition(m

m)

Earlier TTgesturefor /t/than for /d/

S01

Results: hakusai~yakuzai

a /u/

s/z

TD islowerfordevoiced/u/

VerticalPo

sition(m

m)

a

TT riseisearlierafter the

devoiced/u/

kS01

Results: katsutoki~katsudou

a /u/

ts

TD islowerfordevoicedvowel

VerticalPo

sition(m

m)

o

TT releaseisearlierafter thedevoicedvowel

t/d

o

k

S01

Results: masutaa~masuda

TemporaldifferenceinTDrise;devoicedvowelislater

VerticalPo

sition(m

m)

a

a

/u/

d/t

aa

sAndTBrise;devoicedvowelislaterandhigher

S01

Computationalanalysis

10/12/16

5

Analysis

• Voicing effect:do voiced and devoiced voweltrajectories differ?

• Phonological deletion: is thevowel /u/ absent inanyof thesewords?

• Discrete CosineTransform(DCT) onTDtrajectories• Simulate a“targetless” trajectory• Compare DCTcoefficients of“targetless” trajectory todata, c.f.,t-testagainst zero.

Discrete CosineTransform (DCT)ComplexcurverepresentedasthesumofCosines:

1st Cosine

2nd Cosine

3rd Cosine

intercept

Sum of cosines

𝐶 𝑚 cos( 𝜃)where mis the nthcosineand 𝜃 is a functionof length

𝐶 𝑚 = 2𝑁𝑘./ 𝑥 𝑛 cos

2𝑛+ 1 (𝑚− 1)𝜋2𝑁

678

9:;

Where N is the number of data samples;M = 1 ,…,N; 𝑘. =

8

<whenm = 1,else 𝑘. = 1 ;

x(n) isthe intercept;

HowmanyDCTcoefficients areneeded?• Nearly lossless compression (99.6%) with 6coefficients.

• Weused 4DCT coefficients (99.0%)

.996.990

EachcosinecomponentsRaw data (green)Mean DCT (black)[e]-to-[a] line(red)

1stDCT Coeff icientà Average TD height

2nd DCT Coeff icientà V-to-Vtrajectory

3rd DCT Coeff icientà Intervening vowel

4th DCT Coeff icientà Consonantal effects

ea

ea

u

dʃ ʃd

Voweldeletion(“noisynull”)trajectories

Noisy null trajectories (blacklines) generated fromstochastic sampling of Gaussian distributionsdef ined bymean DCTcoefficientsfit tothedirecte-to-atrajectory (redline)and thestandard deviationofDCT coefficients fittotheraw data (greenlines) .

e

a a

eraw data (greenlines)directe-to-atrajectory

(redline)

voiced voicelessWithin-speaker, within-word variation

S01

Some tokens lookmore like noisy null,whereas others looklike they have a cleartarget.

PhoneticreductionOrvariable deletion?

Almost alltokens lookpretty dif ferentfromnoisy null

Noisy null

10/12/16

6

Token-by-tokenevaluation

𝑝(𝐷|𝑐8, . . . , 𝑐D) =𝑝 𝐷 𝑝(𝑐8 , . . . , 𝑐D |𝐷)

𝑝 (𝑐8, … ,𝑐D)

where...𝐷 ={deletion,fullvowel}𝑐8=1st DCTCoefficient𝑐< =2nd DCTCoefficient𝑐F =3rd DCTCoefficient𝑐G =4th DCTCoefficient

Fit a naïve Bayes classif ier tothedata andused it togenerate (posterior) deletion probabilities Trainingdata=

voicedtokens (fulltarget) &noisynull(notarget)Testdata=voicelesstokens

Simulated predictions

Posteriord

eletionp

robabilities

Deletion probabilities bytoken:/ɸusoku/(allspeakers)

Lessthan.1chanceofdeletion

Greater than .9chanceofdeletion

Posteriordeletionprobability

Note: DCTcoeff icients arespeaker-specif ic. Theresults are tallied.

Classificationparameters1stDCT

Coeff icient

2nd DCTCoeff icient

3rd DCTCoeff icient

4th DCTCoeff icient

Greatestseparationfor

3rd DCTcoefficient

/ɸusoku/

Parameters andprobabilities: /ʃutaisei/ (allspeakers)

Posteriordeletionprobability

1stDCT

2nd DCT

3rd DCT

4th DCT

Targetpresent

Targetabsent

Parameters andprobabilities: /katsutoki/(allspeakers)

Posteriordeletionprobability

1stDCT

2nd DCT

3rd DCT

4th DCT

Targetpresent

Targetabsent

10/12/16

7

Parameters andprobabilities: /hakusai/(allspeakers)

Posteriordeletionprobability

1stDCT

2nd DCT

3rd DCT

4th DCT

Targetpresent

Targetabsent

Parameters andprobabilities: /masutaa/ (allspeakers)

Posteriordeletionprobability

1stDCT

2nd DCT

3rd DCT

4th DCT

Targetpresent

Targetabsent

AlltheresultsS01 S02 S03 S04 S05 S06 average

ɸusoku 0.47 0.39 0.75 0.84 0.01 0.19 0.44

ʃutaisei 0.92 0.68 0.84 0.99 0.02 0.89 0.72

katsutoki 0.81 0.19 0.69 0.93 0.06 0.79 0.58

hakusai 0.00 0.00 0.51 0.50 0.00 0.07 0.18

masutaa 0.64 0.09 0.01 0.02 0.74 0.41 0.32

average 0.57 0.27 0.56 0.66 0.17 0.47 0.45

• /ʃutaisei/ shows the highest probability of deletion; /hakusai/ the lowest.• S03 & S04 show high deletion probabilities; S05 barely showed deletion.

Effectofsurfaceconsonantclustertype?• Allthehistograms are bi-modal, supporting theoptional deletion hypothesis.

• Forallspeakerswe find:

ʃ_t >>ɸ_s >>k_s

ts_t

Implicationsforphonologicalorganization σ σ

μ μ

ʃ t a

Possibleconsequencesofdeletionσ σ

μ μ

ʃ u t a

σ

μ

ʃ t a

Resyllabif ication(Kondo2000)

Consonantal syll(Matsui 2015)

10/12/16

8

Moraremains

• Abimoraic truncation pattern (Poser 1980 etseq.)counts moras of a“devoiced” vowelasmoraic(Kawahara 2015; Tsuchida 1997).

• E.g. [suto] <[sutoraiki] (loanword truncation)• E.g. [tʃika(-tʃaɴ)] <[tʃikako] (hypocoristic)• E.g. [ɸuka-ɸuka] (mimetics)

• Hirayama (2009) shows thatdevoiced vowels countasmuch asvoiced vowels forhaiku.

Syllableremains too(?) Ito(1990),Kawahara (2016)

= a wordmustbe bisyllabic;Wd much branch( I&M 1992)

= a devoiced vowel projects its syllable

Syllableremains too(?)

• Predictions from the cross-linguistic perspectives:

σ

μ

ʃ t a

σ σ

μ μ

ʃ t aRising sonority is better; a.k.a.Sonority Sequencing Principle(SSR)

Falling sonorityis better; a.k.a.Syllable Contact Law (SCL)

Effectofsurfaceconsonantclustertype?

• The hierarchywefound supports the second view;i.e.that twoconsonants areseparated by asyllableboundary.

ʃ_t >>ɸ_s >>k_s

(t)s_t t(s)_t• Affricates can variablybe treatedas anordered

fricative-stop segmental complex (Sagey 1986) orstrident stop (Clements 1999) (cf. Lombardi 1990).

Acaveat• Eachphonologicalenvironmentwastestedwithoneitemonly.

• Afollow-upexperimenthasbeenrunwithanadditional6speakersproducingeachofthebelowdyads10-15times(butthedataisyettobeanalyzed).

FS FF SS SFɸuton~ɸudou（布団—不動）ɸutan~ɸudan（負担—不断）ɸuta~ɸuda（ふたー札）

ɸusoku~ɸuzoku（不足—付属）ɸusai~ɸuzai（負債—不在）ɸusagaru~ɸuzakeruふさがる ふざける

kutakuta~kudaranuくたくた-くだらぬkutabaru~kudasaruくたばる くださるkutaniyaki~kudanshita九谷焼 九段下

kusami~kuzai臭みー句材kusari~kuzawa鎖 久沢kusakari~kuzakicho草刈り 久崎町

ʃutaisei~ ʃudaika（主体性—主題歌）ʃutou~ʃudou（酒盗—手動）ʃutokou~ʃudouken首都高 主導権

ʃusai~ʃuzai（主催ー取材）ʃusa~ʃuzan（主査-珠算）ʃuso~ʃuzou主訴 酒造

Entropyalsovariedacrossitems

Temporal stabilityanalysis

• Cross-linguistic workon the articulatory timing ofconsonant clusters has identified timing differencescorrelated withsyllable structure.

• These differences arereflected inpatterns oftemporal stabilityacross CVXand CCVX sequences(Browman andGoldstein, 2007; Shawetal., 2009;Marin, 2012; Hermes etal.,2013; ShawandGafos,2012).

10/12/16

9

Patterns oftemporal alignment

CC

V

… CC

V

…

Heterosyllabic parse(simplexsyllableonsets)

[C.CV] [CCV]SyllableParse

CoordinationTopology

SurfacePattern

V

CC

V

CC

Tatuosyllabic parse(complexsyllableonsets)

On the hypothesis thatthe syllable nucleus is coordinated with thesyllable onset…(Browman and Goldstein, 2000)

Temporalstabilitymetrics

Relative Standard Deviation (RSD)

left center right

.05 .02 .07

Relative Standard Deviation (RSD)

left center right

.12 .07 .04

Following: Browman CP, GoldsteinL(1988) Some Notes onSyllable Structure inArticulatory Phonology.Phonetica 45: 140–155. PMID: 3255974

ʃ ʃ

ʃ ʃ tt

Singletoncontrolwords forstabilityanalysis(alreadyrecorded)

Consonant cluster Singleton control

Entropy = 1.99 (W,K) [mastaa]マスター bataa バター

Entropy = 1.89 (w) [haksai] 白菜 dasai ダサい

Entropy = 1.46 [ʃtaisei]主体性 taisei 体制

Entropy = 1.08 (W,K) [ɸsoku]不足 kasoku 加速

Entropy = 0.09 (W,K) [katstoki]勝つ時 mirutoki 見る時

Methods

Posteriordeletionprobability

Targetabsent

Targetpresent

ʃtai

tai

Bayesian decision rule applied toposterior probabilities

Target absent tokens (n= 138) were compared tosingleton

controls

LE

RECC

AAA

Preliminary results: ʃutaisei

Relative Standard Deviation(RSD)

LE_A CC_A RE_A.23 .18 .11

The right-edge toanchor (RE_A) Interval is the moststable,

an indication of simplex onsets

Data froma totalof 276tokens, 138taisei and 138shutaisei with<.5 probability of voweltarget; NB: data are from5speakers, as one speaker (S05) hadnotargetless /u/ tokens.

Conclusion

10/12/16

10

• The articulatory nature ofdevoiced vowelswasbarelyknown(modulo Funatsu &Fujimoto2011).

• We providedfirstsystematic analyses oftherelevantarticulatory data.

• Voweliseither deleted or retained, butnever (oronlyveryrarely) reduced.

• The likelihoodofdeletionvariesacrossdifferentconsonantalenvironments,butsystematically so.

• There isinter-speaker variation aswell.• Notentirely consistentwithKawakami’s(1971)orWhang’s(2014,2016)predictions.

• Notclear effects ofconsonant-conditionedentropy either.

• Weconjectured thatour results support theheterosyllabic analysis of the “resulting clusters”(Matsui 2005).

• Somephonologicalevidence• Syllablecontact effect• Temporal stabilitypatterns

• Butmore needs tobe done, especially toconfirmthatJapanese follows SyllableContact Law(theemergence of theunmarked?).

Acknowledgements

• Research supported by theJSPSgrants (#26770147and #26284059, and especially #15F15715, whichsupported our collaboration).

• Thanks to JeffMoore and Chika Takahashi fortheirhelpwith theexperiment and EMAanalysis.