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  • Data Collection Data Analysis: Coding Background Summarylearningtotalk.org/sites/learningtotalk.org/files/publications/Johnson... · productions in 2-3-year old children ! Use a psychoacoustically
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Child-Level Factors & Acquisition of the /t/--/k/ Contrast: Production Allison Johnson a , Sara (Bernstein) Cline b , Patrick F. Reidy a , Mary Beckman c , Benjamin Munson b , & Jan Edwards a Background ! What do we know about speech development in children who are typically developing? (Smit et al., 1990; Macken & Barton, 1980) ! What do we know about the /t/--/k/ place contrast and its development in young children? (Stevens, 2000; Hewlett, 1987) ! How is speech characterized in research and in practice? (Munson et al., 2010) ! What are the downsides to transcription? (Munson et al., 2010; Gibbon, 1999; Gibbon, 1990) ! How can acoustic analysis support transcription? (Nicholson et al., 2015; Holliday et al., 2014; Forrest et al., ) ! Why are fine-grained measurements clinically relevant? (Tyler, Figurski, & Langsdale, 1993) Objectives ! Perform spectral analysis of stop- consonant release bursts to describe fine-grained variability in /t/ and /k/ productions in 2-3-year old children ! Use a psychoacoustically relevant measure of frequency—Peak ERB—as a summary measure, rather than a physical measure of frequency (i.e., Hz) ! Develop a Robustness of Contrast measure to describe children’s acquisition of the /t/--/k/ contrast Robustness of Contrast ! Objective measure based on auditory spectral analysis of the stop release burst ! % Tokens correctly predicted by mixed effects logistic regression model Summary ! Peak ERB differentiates [t] and [k] better in back vowel contexts than in front vowel contexts ! Adults show a range in Robustness of Contrast across all vowel contexts (65% - 100%) ! Children show a greater range in Robustness of Contrast, even when analyzing productions that were transcribed as correct (51% - 100%) ! 80% of the adults had at least 90% of their tokens correctly predicted across all vowel contexts ! 29% of the children had at least 90% of their tokens correctly predicted in back vowel contexts only ! None of the child-level variables were significant predictors of Robustness of Contrast Future Directions ! Explore additional measures to help differentiate [t]—[k] in front vowel contexts ! Explore how intermediate productions were classified ! Compare Robustness of Contrast for [t]— [k] productions to other speech contrasts, such as [s]—[ʃ] or [ɹ]—[w] ! Look at change in Robustness of Contrast over time ! See if Robustness of Contrast in 2 ½-3-year-old children predicts any child- level variables one year later ! See how Robustness of Contrast varies across different populations (e.g., children with cochlear implants) Coding ! Praat 1. Segment word boundaries and code response context 2. Manner transcription (stop, other) 3. Place [narrow] transcription Spectral Analysis: Analyzable Data: ! Stop productions ! Transcribed as [t], [k], or intermediate ! VOT > 20ms ! Unobscured by background noise To Compute Peak ERB: ! From the .WAV recording, extract 5ms preceding burst through 20ms following burst with a rectangular analysis window ! Estimate the spectrum of the window using a Multitaper spectrum (K=8, NW = 4) ! Pass the spectrum through a gammatone filter bank (to better represent the human auditory filter) ! Pass the spectrum through a high-pass filter (to reduce contamination of the signal due to ambient background noise) ! Output will show a psychoacous/c spectrum rela/ng excita/on in a gammatone filter to its center frequency along the ERB scale ! The frequency with the greatest amplitude is the Peak ERB— our summary acous/c measure To Calculate Robustness of Contrast: ! Mean-center Peak ERB to improve interpretability of the model ! Build a mixed effects logistic regression model to predict Target Consonant (either /t/ or /k/) from Peak ERB values: TargetConsonant ~ PeakERB + VowelContext + PeakERB * VowelContext + (1 + PeakERB | ID) ! Calculate the log-odds for each production: what is the likelihood that the production is a [t] or [k], given its Peak ERB and the vowel context ! Calculate the accuracy of each prediction ! Calculate the % of each child’s tokens that were correctly predicted by the model Acknowledgements Supported by: • NIDCD grant R01 02932 to Jan Edwards • NICHD P30HD03352 grant to the Waisman Center • T32 Training Grant DC05359-10 to Susan Ellis Weismer Special Thanks: Learning to Talk lab members for helping collect, code, and analyze data. Learning to talk par/cipants and their families for making this work possible. Visit learningtotalk.org for more informa/on about our lab and a handout of this poster. a University of Wisconsin-Madison b University of Minnesota-Twin Cities C Ohio State University Data Collection Participants !n = 111 !Females = 51, Males = 60 !Living near Madison or Minneapolis !Monolingual, native English speakers !Normal hearing !Late Talkers (n = 11) !African-American English speakers (n = 14) and Mainstream American English speakers (n = 97) Data Analysis: Coding ASHA Annual Convention, Nov. 2015 Visit Tasks ! Demographic Questionnaire (parent task) ! Language Environment Analysis (LENA™) ! Hearing Screening ! Expressive Vocabulary Test—2 nd edition (EVT-2; Williams, 2007) ! Peabody Picture Vocabulary Test—4 th edition (PPVT-4; Dunn & Dunn, 2007) ! Goldman-Fristoe Test of Articulation—2 nd edition (GFTA-2; Goldman & Fristoe, 2000) ! Minimal Pair Discrimination Task ! Real-Word Repetition Task Real-Word Repetition Task: Stimuli ! 34 productions of /t/- and /k/-initial words ! 17 different, familiar words ! Presented aloud from a computer ! Paired with picture on the screen ! 16 /t/ tokens, 8 in back vowel contexts ! 18 /k/ tokens, 8 in back vowel contexts 4. Tag locations of release burst and VOT Transcription Categories Target /t/ ! [t] : clear, correct [t] ! [t:$k] : intermediate, closer to correct ! [$k:t] : intermediate, closer to incorrect ! [$k] : clear substitution of [k] for /t/ Target /k/ ! [k] : clear, correct [k] ! [k:$t] : intermediate, closer to correct ! [$t:k] : intermediate, closer to incorrect ! [$t] : clear substitution of [t] for /k/ Maternal Educa5on n Low 14 Middle 26 High 70 Child Characteris5c n Mean (SD) Range Age (months) 111 32.6 (3.5) 28 - 39 EVT-2 standard score Norm: 100 (15) 109 116 (17) 81 - 160 PPVT-4 standard score Norm: 100 (15) 109 113 (18) 79 - 153 Adult Norms: ! To determine how well Peak ERB differentiates [t] and [k] productions, we tested the models on 16 adult speakers Results: ! [t] and [k] were better differentiated in back vowel contexts compared to front vowel contexts for both children and adults ! Children’s productions were highly variable ! Children had a greater range in Robustness of Contrast measures compared to adults, even for productions transcribed as correct Effects of between-category distance and within-category dispersion on discriminability. Top graph shows poor discriminability due to low between-category distance (a) and high within-category dispersion (b). Bodom graph shows good discriminability due to high between-category distance (a) and low within-category dispersion (b).

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Page 1: Data Collection Data Analysis: Coding Background Summarylearningtotalk.org/sites/learningtotalk.org/files/publications/Johnson... · productions in 2-3-year old children ! Use a psychoacoustically

Child-LevelFactors&Acquisitionofthe/t/--/k/Contrast:ProductionAllisonJohnsona,Sara(Bernstein)Clineb,PatrickF.Reidya,MaryBeckmanc,BenjaminMunsonb,&JanEdwardsa

Background

!  Whatdoweknowaboutspeechdevelopmentinchildrenwhoaretypicallydeveloping?(Smitetal.,1990;Macken&Barton,1980)

!  Whatdoweknowaboutthe/t/--/k/placecontrastanditsdevelopmentinyoungchildren?(Stevens,2000;Hewlett,1987)

!  Howisspeechcharacterizedinresearchandinpractice?(Munsonetal.,2010)

!  Whatarethedownsidestotranscription?(Munsonetal.,2010;Gibbon,1999;Gibbon,1990)

!  Howcanacousticanalysissupporttranscription?(Nicholsonetal.,2015;Hollidayetal.,2014;Forrestetal.,)

!  Whyarefine-grainedmeasurementsclinicallyrelevant?(Tyler,Figurski,&Langsdale,1993)

Objectives

!  Performspectralanalysisofstop-consonantreleaseburststodescribefine-grainedvariabilityin/t/and/k/productionsin2-3-yearoldchildren

!  Useapsychoacousticallyrelevantmeasureoffrequency—PeakERB—asasummarymeasure,ratherthanaphysicalmeasureoffrequency(i.e.,Hz)

!  DevelopaRobustnessofContrastmeasuretodescribechildren’sacquisitionofthe/t/--/k/contrast

RobustnessofContrast

!  Objectivemeasurebasedonauditoryspectralanalysisofthestopreleaseburst

!  %Tokenscorrectlypredictedbymixedeffectslogisticregressionmodel

Summary

! PeakERBdifferentiates[t]and[k]betterinbackvowelcontextsthaninfrontvowelcontexts

! AdultsshowarangeinRobustnessofContrastacrossallvowelcontexts(65%-100%)

! ChildrenshowagreaterrangeinRobustnessofContrast,evenwhenanalyzingproductionsthatweretranscribedascorrect(51%-100%)

! 80%oftheadultshadatleast90%oftheirtokenscorrectlypredictedacrossallvowelcontexts

! 29%ofthechildrenhadatleast90%oftheirtokenscorrectlypredictedinbackvowelcontextsonly

! Noneofthechild-levelvariablesweresignificantpredictorsofRobustnessofContrast

FutureDirections

! Exploreadditionalmeasurestohelpdifferentiate[t]—[k]infrontvowelcontexts

! Explorehowintermediateproductionswereclassified

! CompareRobustnessofContrastfor[t]—[k]productionstootherspeechcontrasts,suchas[s]—[ʃ]or[ɹ]—[w]

! LookatchangeinRobustnessofContrastovertime

! SeeifRobustnessofContrastin2½-3-year-oldchildrenpredictsanychild-levelvariablesoneyearlater

! SeehowRobustnessofContrastvariesacrossdifferentpopulations(e.g.,childrenwithcochlearimplants)

Coding!  Praat1.  Segmentwordboundariesand

coderesponsecontext

2.  Mannertranscription(stop,other)3.  Place[narrow]transcription

SpectralAnalysis:AnalyzableData:!  Stopproductions!  Transcribedas[t],[k],orintermediate!  VOT>20ms!  Unobscuredbybackgroundnoise

ToComputePeakERB:!  Fromthe.WAVrecording,extract5msprecedingburstthrough

20msfollowingburstwitharectangularanalysiswindow!  EstimatethespectrumofthewindowusingaMultitaper

spectrum(K=8,NW=4)!  Passthespectrumthroughagammatonefilterbank(tobetter

representthehumanauditoryfilter)!  Passthespectrumthroughahigh-passfilter(toreduce

contaminationofthesignalduetoambientbackgroundnoise)!  Outputwillshowapsychoacous/cspectrumrela/ng

excita/oninagammatonefiltertoitscenterfrequencyalongtheERBscale

!  ThefrequencywiththegreatestamplitudeisthePeakERB—oursummaryacous/cmeasure

ToCalculateRobustnessofContrast:! Mean-centerPeakERBtoimproveinterpretabilityofthe

model!  Buildamixedeffectslogisticregressionmodeltopredict

TargetConsonant(either/t/or/k/)fromPeakERBvalues:TargetConsonant~PeakERB+VowelContext+PeakERB*VowelContext+

(1+PeakERB|ID)!  Calculatethelog-oddsforeachproduction:whatisthe

likelihoodthattheproductionisa[t]or[k],givenitsPeakERBandthevowelcontext

!  Calculatetheaccuracyofeachprediction!  Calculatethe%ofeachchild’stokensthatwerecorrectly

predictedbythemodel

Acknowledgements

Supportedby:•  NIDCDgrantR0102932toJanEdwards•  NICHDP30HD03352granttotheWaismanCenter•  T32TrainingGrantDC05359-10toSusanEllisWeismer

SpecialThanks:LearningtoTalklabmembersforhelpingcollect,code,andanalyzedata.

Learningtotalkpar/cipantsandtheirfamiliesformakingthisworkpossible.Visitlearningtotalk.orgformoreinforma/onaboutourlabandahandoutofthisposter.aUniversityofWisconsin-MadisonbUniversityofMinnesota-TwinCitiesCOhioStateUniversity

DataCollectionParticipants! n=111! Females=51,Males=60! LivingnearMadisonorMinneapolis! Monolingual,nativeEnglishspeakers! Normalhearing! LateTalkers(n=11)! African-AmericanEnglishspeakers(n=14)andMainstreamAmericanEnglishspeakers(n=97)

DataAnalysis:Coding

ASHAAnnualConvention,Nov.2015

VisitTasks! DemographicQuestionnaire(parenttask)! LanguageEnvironmentAnalysis(LENA™)! HearingScreening! ExpressiveVocabularyTest—2ndedition

(EVT-2;Williams,2007)! PeabodyPictureVocabularyTest—4thedition

(PPVT-4;Dunn&Dunn,2007)! Goldman-FristoeTestofArticulation—2ndedition

(GFTA-2;Goldman&Fristoe,2000)! MinimalPairDiscriminationTask! Real-WordRepetitionTask

Real-WordRepetitionTask:Stimuli! 34productionsof/t/-and/k/-initialwords! 17different,familiarwords! Presentedaloudfromacomputer! Pairedwithpictureonthescreen! 16/t/tokens,8inbackvowelcontexts! 18/k/tokens,8inbackvowelcontexts

4.TaglocationsofreleaseburstandVOT

TranscriptionCategoriesTarget/t/! [t]:clear,correct[t]! [t:$k]:intermediate,closertocorrect! [$k:t]:intermediate,closertoincorrect! [$k]:clearsubstitutionof[k]for/t/Target/k/! [k]:clear,correct[k]! [k:$t]:intermediate,closertocorrect! [$t:k]:intermediate,closertoincorrect! [$t]:clearsubstitutionof[t]for/k/

MaternalEduca5on nLow 14Middle 26High 70

ChildCharacteris5c n Mean(SD) Range

Age(months) 111 32.6(3.5) 28-39

EVT-2standardscoreNorm:100(15)

109 116(17) 81-160

PPVT-4standardscoreNorm:100(15)

109 113(18) 79-153

AdultNorms:!  TodeterminehowwellPeakERBdifferentiates[t]and[k]

productions,wetestedthemodelson16adultspeakers

Results:! [t]and[k]werebetterdifferentiatedinbackvowelcontexts

comparedtofrontvowelcontextsforbothchildrenandadults! Children’sproductionswerehighlyvariable! ChildrenhadagreaterrangeinRobustnessofContrastmeasures

comparedtoadults,evenforproductionstranscribedascorrect

Effectsofbetween-categorydistanceandwithin-categorydispersionondiscriminability.Topgraphshowspoordiscriminabilityduetolowbetween-categorydistance(a)andhighwithin-categorydispersion(b).Bodomgraphshowsgooddiscriminabilityduetohighbetween-categorydistance(a)andlowwithin-categorydispersion(b).

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