ISSNO300-1067

ＳＴＵＤＩＡ
ＰＨＯＮ ＯＬＯＧＩＣＡ
音 声 科学研究

ｘｘｖｍ［
ＩＮＳＴＩＴＵＴＩＯＮＦＯＲＰＨＯＮＥＴＩＣＳＣＩＥＮＣＥＳ
ＫＹＯＴＯＵＮＩＶＥＲＳＩＴＹ
１９９４

ＡｃｏｕｓｔｉｃＡｎａｌｙｓｉｓａｎｄＴｒａｎｓｃｒｉｐｔｉｏｎｏｆ
ＬｉｎｇｕｉｓｔｉｃａｎｄＰａｒａｌｉｎｇｕｉｓｔｉｃＦｅａｔｕｒｅｓｉｎ
ＤｉａｌｏｇＵｅＳｐｅｅｃｈ
ShigeyoshiKitazawaザSatoshiKobayashii
TakaoMatsunaga;Hideyalchikawa:tandJunichiNishiyamat
Abstract
Nonverbalfeaturesofspontaneousspeechareimportantinhumandialogue・Andtranscriptionofdialogueincludingtheparalanguageisausefultechniquefbranalysisofthedialogues・
But,theparalinguisticfeaturesaretranscribedbytranscriber，ssubjectivenow、Somefeaturesaremeasurableasconcreteacousticalfeatures・Wemademeasurementofthespeech
ratethroughspectrogramtransfOrmationofthewaveenvelopeandautomaticestimationof
suchfeaturesishelpfUlintranscribingthedialoguesJnthispaper，ｗｅｅｘａｍｉｎｅａｎａｕｔｏｍａｔｉｃ
ｍｅｔｈｏｄｏｆｓｐeechrateestimationanｄstudiedconsistencyofdescriptionsbetweendifIerent

transcribers．

ｌｌｎｔｒｏｄｕｃｔｉｏｎ
Nonverbalcommunicationsplayanimportantroleinhumandialoguewhereparticipants
usenaturalspeechsocalledspontaneousspeech、ThatnonverbalinfOrmationincludingthe

vocalizationsandthevoicequalitiesiscalledasthe，'paralanguage，'[1ITherearenumber
ofaspectsofparalanguage、Ｓｏｍｅofthemaresensationalfeatures，ａｎｄdifficulttorealize
measurements、Ｔｈｅothers，however，aremeasurableasconcreteacousticalfeaturessuchas
loudness,pitch，ａｎｄｓｐｅｅｃｈｒａｔｅＨｅｒｅｗｅｄｅｓｃｒｉｂｅｄｈｏｗｔｏｍｅａｓｕｒｅｔｈｅｓｐｅｅｃｈｒａｔｅ・Anotheraspectofdialogueresearchisthetranscriptionintotextoftheparalinguisticfeaturessuchas
voiceloudness,tone，ａｎｄspeechrateaswellasutterances、Wemadevariousmeasurements
ofvalidityandconsistencyofdescriptionsbetweendifTerenttranscribers．
＊ShigeyoshiKitazawa(北澤茂良)：Professor,DepartmentofComputerScience,FacultyofEngineering，

ShizuokaUniversity↑SatoshiKobayashi(小林聡):DoctorCource,TheGraduateSchoolofElectronicScienceandTechnology，ShizuokaUniversity

上TakaoMatsunaga(松永隆雄),Hideyalchikawa(市川英哉),JunichiNishiyama(西山淳一):MasterCource，DepartmentofComputerScience，FacultyofEngineering，ShizuokaUniversity

11

ShigeyoshiKItazawaetaL

1２

ｚＤｅｆｉｎｉｔｉｏｎｏ盃ＳｐｅｅｃｈＲａｔｅａｎｄＭｅａｓｕｒｅｍｅｎｔＭｅｔｈｏｄ
AmoraisaprosodictermthatisaconjunctionofaconsonantandashortvoweLAmora
isanotherphonemeoraphonemeconjunctionoftheequivalentlengthAmoraconsistsa

structureｏｆ/V/,／vV/,/CV/,/CvV/,/N/,ａｎｄ／Q／(Ｖ:avowel,ｖ:asemivowel,andOa

consonant)JnstudyingJapanesephoneme,ａｍｏｒａｉｓａmorerealisticandconvenientunit
thanthesegmentalphoneme・Ｔｈｅｓｐｅｅｃｈｒａｔｅｉｓｔｈｅｎｕｍｂｅｒｏｆｍｏｒａspersecond
Onetechnicalmeasurementoftherateofspeechisachievablethroughthephoneme
recognition，ｔｈａｔｉｓ，ｔｈｅｐｏｉｎｔｏｆｔｉｍｅｏｆｅａｃｈｐｈｏｎｅｍｅｉｓｍarkedalongthetimelineandtheresultingphoneme(hencethemora)lengthsareaveragedalongsomeintervalsresulting
anumberofmoraspersecond、Thisdefinition，however，ｉｓdifHculttorealizewiththeautomaticspeechrecognitiontechnology，butpossibleonlybyhandlabeling・ThereareseveralpossiblehypothesｅｓｏｆｐｅｒｃｅｐｔｉｏｎｏｆｔｈｅｒａｔｅｏｆｓｐｅｅｃｈＷｅｅｓｔｉｍａｔｅｔｈｅｔｅｍｐｏ
ｗｉｔｈoutidentificationofthephonemea､ddonotexploitspeechrecognition,ｈｅｎｃｅｗｅａｓｓｕｍｅ
ｗｅｃａｎｒｅｃｏｇｎｉｚｅｔｈｅrateofspeechwithoutrecognizingthecontentofspeech・Furthermore
wecanperceiveうfbrexample1theutterancespeeｄfromthenarrowbandfilteredspeecｈsound、Thisfactsuggeststhatoursenseofａｔｅｍｐｏｃａｎｂｅｐｅｒｃｅｉｖａｂｌｅｆｒｏｍｔｈｅｅnvelopeof
thewavefbrm、Intheprecedingstudy,therhythmiscorrelatedtotheintervalofthecenter
ofenergybetweenadjacentsyllables[２１BecauseJapanesehastheCV-syllable-timedfeature,downswingsoftheenvelopeappear
ateveryconsonantsegmeｎｔａｌｍｏｓｔａｔｔｈｅｓａｍｅｉｎｔｅｒｖａＬＡｓｐｅｅchenvelopechangesdynamicallyｆｒｏｍａｃｏｎｓｏｎａｎｔｔｏａｖｏｗｅｌａｎｄｔｈｅｎｔothenextconsonantfbrmingthepeaksand
thevalleys・Theintervalsbetweenpeaksandvalleysareexpectedtobeapproximatelyequal
orperiodicbecauseofthesyllable-tilnedfbaturelfthisistrue，thenwecanextractthis
periodicitythroughthefbllowingprocedure:ｔｈｅＤＦＴ(DiscreteFourierTransfbrm)ofthe
Hammingwindowedenvelopepatterｎ．Weemployedwindowsizeaboutonesecondthroughourexperiment・Thewindowmcludeslocalpausesandnon-lexicalvoicingduetonon-verbal
orparalinguisticexpressions・
Thespeakingrateisobservedasadominantspectralpeakinafrequencyｄｏｍａｉｎ，where

thespeakingrateisvisualizedinthefrequency-timeplanelikethefbrmanticpatternof
spectrograms・Thefrequencyandthetimearescaleｄｄｏｗｎｗａｒｄｔｏｏｎｅｔｗｏｈｕｎｄｒｅｄｔｈｏｆｔｈｅ８ｋＨｚｓａｍｐlingrateofthenormalspectrogram・Wecouldobservegraygauged
monochｒｏｍｅｐａｔｔｅｍｓｉｎｔｈｅ２０Ｈｚｆｒｅｑｕｅｎｃｙｒegionwithonesecondoftimewindow．
０

２

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1００
００

1０００

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ＦＦｅｑｕｅｎｃ９（Ｈｚ）

Figurel：２８channelauditory filter．

１０１００１０００ＢＯＯＯ

Ｆｒｅｑｕｅｎｃ９（Ｈｚ）

Figure2：８０ＨｚＦＩＲｌｏｗｐａｓｓｆｉｌｔｅｒ．

AcousticAnalysisandnanscriptionofLinguisticandParaIinguisticIneatures

1３

Weemployedthebandpassfilteredｓｐｅｅｃｈwiththeauditorymodelfbrthesourceofthe
speechenvelopeThecharacteristicoftheauditoryfilterissｈｏｗｎｉｎＦｉｇｉｕｒｅＬＩｎｏｒｄｅｒｔｏ
ｏｂｔａｉｎａｎｅｎｖｅｌｏｐｅｏｆｔｈｅｓｐｅｅｃｈｗａｖｅｆｂｒｍ，wefirstrectifiedthewavetoobtainahalfLwave，
onwhichthenwelowpass-filteredtoobtainanapproximateenvelope・Wedesignedalow
passfilterofthecｕｔｏｆＩｆｒｅｑｕｅｎｃｙａｔ８０ＨｚｔｏｋｅｅｐｔｈｅｅnvelopedetailsandtoeliminatehighfrequencyfluctuationsduetopitchandfOrmantfrequenciesaswelLThisfilterdealsabout
tentimesoftheaveragerateofspontaneousｓｐｅｅｃｈ，８ｍｏｒａｐｅｒｓecond・Thecharacteristic
ofthelowpassfｉｌｔｅｒｉｓｓｈｏｗｎｉｎＦｉｇｕｒｅ２・Ｉｎｂｏｔｈｔｈｅｓｐｅｅｃｈｗａｖｅｓｏｆｂａｎｄｐａｓｓfilteredandfullband）wecouldobserveinthespectrogramofthewaveenvelopesuchconcentrated
spectralenergyaroundthefrequencycorrespondingtothespeechrate．

ＳＳｐｅｅｃｈＲａｔｅＥｓｔｉｍａｔｉｏｎｏｎＳｙｎｔｈesizedWavefbrm
WeexaminedourmethodwithsynthesizedenvelopewavefOrmsasapreliminaryexperiment、WeusefburenVelopewavesasfbUows・Ａｌｌｏｆｔｈｅｍａｒｅｈａｌｆｗａｖｅｓｉｎｕｓｏｉｄｓｒectifiedtosimulatetheenvelopｅｏｆｖｏｗｅｌｓｅｇｍｅｎｔｓ・Ｔｈｅｈａｌｆｗａｖｅｓａｒｅａｒｒａｎｇｅｄｓｏａｓｔｏｂeproportional
totheinverseofthespeechrateorarrangedalongthecorrespondingｔｏｔｈｅｐｅｒｉｏｄａｓｌｏｎｇ
ａｓ２ｓｅｃｏｎｄ

Ａ：Stationaryperiodwithashortconsonantalgapbetweentheadjacenthalfwaves．
Ｂ：GraduallydecreasingperiodbydecｒｅａｓｉｎｇｔｈｅｇａｐｓｏｆＡ．

Ｃ：StationaryperiodButfrequenciesofahalfwaveincreasegradually,therefbrethegapsincreasegradually．
Ｄ：Adjacenthalfwavesmakeanoverlap、Andtheperiodgraduallydecreases,henceoverlapincreaseswhile
theshapeofhalfwavesinusoidsiskept、Figure3:Ａ～DshowtheseenvelopesandFigure4:Ａ～DshowthespectrogramslnFigure
4,wecouldobserveadarkbarcorresponｄｓｔｏｔｈｅｓｐｅechrate、FromFigure3andFigure4，
thismethodcouldcatchwiderangesofperiodicity，therefbrethiswillestimatespeechrates
basedontheintervalsbetweenpeaksoftheenVelope・Theresultwasindependentofthe
dutyratioofthehalfwavesinusoid，ｉｅ.，ｔｈｅｐｅａｋａｎｄｔｈｅｄｉｐｒａｔｉｏ、Ｉｎｔｈｅｅｎｄｏｆｔｈｉｓpreliminaryexperiment，ｗｅｕｓｅａｓｙｎｔｈｅｓｉｚｅｄｗａｖｅｂａｓｅｄｏｎｔｈe

JapaneseIhythmrule[2]EnvelopeandspectrogramareinFigure5and6Figure7showS

manuallycalculatedspeechrateofFigure5、ThespectrogramofitshowsamorecomplicatedtexturethanthesimpletestsignalsinFigure3and4．

４ＳｐｅｅｃｈＲａｔｅＥｓｔｉｍａｔｉｏｎｏｎＲｅａｌＤｉａｌｏｇｕｅｓ
ThenweexaminedrealdialoguespeechestakenfromTVprograms・Thetestsethasl3
utterances・Sevenutteranceswerespokenbyamalespeaker，sixutteranceswerespoken
byafemalespeaker・Thesamplingfrequencyis8000HzandthedurationsarethreetosevensecondｓＴｈｅｒｅａｌｓｐｅｅｃｈｒａｔｅｓｏｆｔｈｅｔｅｓｔｓｅtaremeasuredbyhandlabelingof
individualphonemes、ContentsofthetestsetutterancesａｒｅｏｎＴａｂｌｅＬＡｎｄａｎｏｖｅｒｖｉｅｗ
ｏｆａｕｔｏmaticestimationprocessisshowniｎＦｉｇｕｒｅ８・Werectifiedthewavetoobtaina

halfLwaveatfirst(b)．Then,welowpass-filteredtoobtainanapproximateenvelope(c)．At
last,ｗｅcomputeDFTsofextractedenvelope(d)．
Weemployedthebandpassfilteredspeechwiththeauditorymodelfbrthesourceofthe
speechenvelope・CutofTfrequencyofthelowpassfilｔｅｒｉｓ４０Ｈｚ．

Shi8WoshiKItazawaetal

1４

，１

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Ｂ：Decreasingperiod

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耐 lｌＷｌｌ

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Ｄ：Decreasingperiodanddips．

Ｃ：Increasingsilentgaps

Figure3：Synthesizedenvelopes． (eachwavecontinues2seconds）

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－－，

--｣二（

Ａ：StationaryperiodB：DecreasingperiodC：IncreasingsilentD：Decreasingperiod
gaps．ａｎｄｄｉｐｓ

Ｆｉｇｕｒｅ４：Spectrogramsofsynthesizedenvelｏｐｅｓｍｔｈｅｒａｎｇｅｏｆ２０Ｈｚａｎｄ２ｓｅｃｏｎｄｓ．
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Figure5：Rule-basedsynthesizedenvelopei、２．８seconds．

Figure6：Spectrogramoftherule-based
synthesizedenvelope(Figure5)intherange
of20HzandO5～2.4seconds．

Ｕ０ｕへ⑪Ｈ０Ｅ〉ＵＵ呵国具０００口⑩

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Figure7:Periodofsynthesisdata(Figure5)．

AcousticAna1ysisandnanscliptionofLinguMcandParalinguisticI71eatures

1５

Ｔａｂｌｅｌ：Utterancesofthetestset．
１２３４５６７

ＭＭＭＭＭＭＭ ＦＦＦＦＦＦ

itaidokogaitaitokanandakandaiwanaikatadesitakara
soudesunehanohnagaisihnnankadatohzuttomottenakyanannaindeh
owarinohouninarutotegasibiretekityaundesuyoh

ehdemoitijikanitijikanguraidesukaramahnijikanwasitijikaosokutomohatijiguraidesune

ndehkujiguraikaranorihajimeteh nU1guralma dｅｄｅｓｕｋａｎｅｈ
nottedesaigomatatyokottoumanoteiresitekaerun●●●●

１２３４５６nitiyoubigayasumikatoomottandesukedonehumanokeikogaarimasiteｔｙｏｕｂａｇａ
ｍａｉｍａｗａｇｅｎｄａｉｔｅｋｉｇｅｎｄａｉｇｅｋｉｏｙａritaimonodatoomotterassyarusoudegozaimasuga
ｄｅｍｏｓｏｎｏｓａｎｎｉｎｎｏｏｈｄｉｓｙｏｎｎｏｈｏｋａnohitotatimominnaneoagezunisoreｄｅｍｏ
utinikityattandesuyonehhagakimitainamondesoredenangatunannitini
oatumarikudasaitteittaramoukonaihitogairuwakejanai
uhndakarasouyunottesugokuyokuwakarujanannanikatakuoyakusokusitanonittehｍａｙｏｒｕｇｏｈａｎｇａｏｗａｔｔｅｏｔｙａｔｏｎｉｔｉｂｕｔｔｅｙｕｋｏｔｏｎａｎｄｅｓｕｋｅｄｏｓｏｋｏｄｅｍａｔaanosehza
taihendesusoredeoyasumigaitinitimonaitteyukotowanitiyoubiha

Table2：MeasurementsofutterancesinTablel．
Ｂ

○

Ｅ

Ｇ

ｍａｎｕａｌ cｏｍｐ ｕｔｅｄ

１１１１●●●●●●

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９８９０７７*:standarddeviatiOnofdifTerences．

●●●●●●●

１●●■●●●

４６８６７０９８７７９０１●●●■□●

1０．０

E=B-D:difTerence,Ｆ＝|Ｅ|/Ｂ×100:errors[%],Ｇ＝Ｃ／Ａ:globalspeechrateinmora/sec，

ｍａｎｕａｌｃｏｍｐｕｔｅｄ:manuallyestimatedspeechrate，ＤＦＴｃｏｍｐｕｔｅｄ:computationallyestimatedspeechrate，head:ｓｐｅｅｃｈｒａｔｅｉｎｂｅｇｉｎｎｉｎｇｌｓｅｃ.，ｍｉｄ:speechrateiｎｔｈｅｍｉｄｄｌｅｌｓｅｃ.，
tail:ｓｐｅｅｃｈｒａｔｅｉｎｌｓｅｃａｔｔｈｅｅｎｄ１

６７１４３１０８０５０８６８●●●●●■●

１１

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9.6

１１

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１１

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8.8

ShigeyoshiKItazawaetal．

1６

a：OriginalｓｏｕｎｄｗａｖｅｏｆｔｈｅｕｔｔｅｒａｎｃｅＭＬにＤＦｌｐｕ一・一Ｐ。』岸か菫ＬＬ，、，味，トＤｒＤ

Ｍ

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し

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Figure8：Processofspeechrateestimationproceedsfromthetop(a)downwardtoobtain
theDFTpeaks．

AcousticAnally)sisandnanscriptionofLinguMcandParalinguMcIneatures

1７

ＷｅｃｏｍｐｕｔｅｄＤＦＴｓｏｆｔｈｅｅｎｖｅｌｏｐｅｓｔｏfindthefrequencyofpeakenergyasanestimatedspeechratewithonesecondhammingwindow．

Ｔｈｅｓｐｅｅｃｈｒａｔｅｃｏｍｐｕｔｅｄａｓｔｈｅｍｏｒａpersecondfluctuatesduetoparalinguisticfeatures，Thespeechrateestimatedastheinverseofperiodbetweenthecenterofpoweroftheadjacentvowelsisdependentofpausesandnonlexicalitemsinthedialogue､Wecompared
thelocalspeechratebyhandlabelingandcomputing．
Table2showsmanuallyestimatedspeechratesandcomputedspeechratesaround，，head，，，"middle”ａｎｄ，，tail，，ｏｆdata、ThelnanualestimationandtheDFTestimationcorrelatedwith

Coefficient0.55．Figure9showsthiscorrelation．

ＦｉｇｕｒｅｌＯａｎｄｌｌａｒｅｅｎｖｅｌｏｐｅｓａｎｄｓｐectrogramsoftherealspeechtakenｆｒｏｍＴａｂｌｅ
ＬＷｅｃａｎｓｅｅｔｒａｃｅｓｏｆｃｏｎｔinuouschangesofspeechrateinthespectrograms，however1
thetracesarenotuniquefbreachspectrogramduetoaliasingoftheharmoniccomponents・
ThespectrogramsshowmorecomplicatedtexｔｕｒｅｔｈａｎｔｈｅｔｅｓｔｓｉｇｎａｌｓｉｎＦｉｇｕｒｅ４，ａｎｄlook
similartoFigure6．

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manual(mora/sec）

Figure9：CorrelationbetweenmanualandDFTcomputedspeechrates．

Itwasdifliculttorecognizethespeechrateasauniquedarkbarpattem．

Figurel2showsspectrogramsofauditoryfilteredresults・Somechannelsareshowbetter

resultthanthenon-filteredone．

薯

ShigeyoshiKitazawaetaL

1８.･･･・含「〒ｒＴＴ７下７７

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AcousticAnalysisandnanscziptionofLinguisticandParalinguisticFeatures

1９

S1nter-transcribｅｒｓｃｏｎｓｉｓｔｅｎｃｙｏｆｔｒａｎｓｃｒｉｐｔｉｏｎ
nxtencodeddialogueisausefUlfbrmtoanalyzedialoguephenomena・However,paralinguisticfeaturesareclassifiedbytranscribers，subjective．Ｉnconsistenciesbetweentranscribers，omissionsandmisleading,areunavoidable・Suchinconsistencyistheproblemfbranalysisof
dialogues・Ｗeestimatedthoseerrorsandinconsistenciesbasedonthecross-checkbetween

transcribers・

Ｗｅｕｓｅａｒｅａｌｓｐｅｅｃｈｄａｔａｔｈａｔｈａｓａｂｏut40seconds，durationsfromaTVprogramofinterviewingbetweenafemaleinterviewerandamaleintervieweelntheprogram，speakers
talkedfreelyeachother・SubjectsheardtherecordeddialogueonSPARCStationlOwith
replayprogramandheadphoneTheprogramcanreplaydesignatedpartbytheuser・
SubjectsusetheHiragana(Japanesesyllableorientedcharacters)totranscribeverbal
soundsandnonverbalsounds，ｅ､９．，laughTheTEInotationstranscribenonverbalfeatures[3]・SubjectsarefivepeopleTheyattendedtomeetingstolearnaboutthetranscription，buttheydidn，thaveanypractices，
WeusedthefOllowingfbrmulatoestimateconsistencyrates．

R=鵲ⅢＯＩ%］
Ａ：Numberoftranscriptionitems(Hiraganas,Tags,andEntities)occurredinoneoftranscriptions
B：ＮｕｍｂｅｒoftranscriptionitemsinanothertranscriptiontobecomparedwithA・
ａ：ＮｕｍｂｅｒofmatcheditemsintranscriptionA．
ｂ：ＮｕｍｂｅｒofmatcheditemsintranscriptionB．

（ａ＝ｂ）

６Results

6.1Consistencyofinter-transcribers
Table3showsconsistencyrates・ＩｎＴａｂｌｅ３，，，Char，，meansHiraganafbrverbalsounds，
and，，Tag，，meansnonverbalfeatures，ｉ､e､，TagsandEntities・Criteriafbreachconsistency
estimationarefbllowings：
Ａ：Ｃｏｕｎｔonlymatchedtranscriptionunits．
Ｂ：CriterionAorthesamecontentstranscribｅｄａｒｏｕｎｄｔｈｅｎｅａｒｐｏｓｉｔｉｏｎｉｎａｒｅｉｎcluded
C：CriterionAandthesimilarcontentｓｔｒａｎｓｃｒｉｂｅｄａｔｔｈｅｓａｍｅｐｏｓｉｔｉｏｎａｒｅｉｎｃｌｕｄｅｄ

Ｄ：CriteriaA，Ｂ，ａｎｄＣａｒｅｉｎｃｌｕｄｅｄ
Ｅ：CriterionDandthesimilarcontentstranscribedaroundthenearpositionareincluded

Here，，，similar，，means
LDifTerencesinlengthnotation,ｅ､9.,'，e-'，(''－，，representsalongvowel)ａｎｄ''ee，，aresimilar、
２ＡｔｒａｎｓｃｒｉｂｅｒｒｅｍａｒｋｅｄｔｈｅｓｏｕｎｄｉｓｎｏｔｄｅｆｉｎｉｔｅｂｕｔｃａｎｂｅｈｅａｒｄｔｏｏｔｈｅｒｓｏｕｎｄＴｈｅｒｅａｒｅｓｕｇｇｅｓｔｉｏｎｓｔｈａｔｍatchinbothtranscriptions、
３．DifTerencesindegreeofchangeofnonverbalfeatures1e､９．，ｉｎｃａｓｅｏｆｔｅｍｐｏｓ），，ａａ，，ａｎｄ，，ａ，jaresimilar．

一一彗轡■透彗ご璽雪亀呰汀智『ShigeyoshiKitazawaetal

2０

Ｔａｂｌｅ３：Consistencyrates
CriteriomAConsistency(％）
6,698(699％）
5,750(868％）
948(320％）

ＡＬ
ＡＬＬ

Ｃｈａｒ
Ｔ aｇ

Inconsistency(％）
2,891(301％）
877(132％）２０080
2,014(680％）

ＴＯＴ
ＴＯＴＡＬ

9，５８９
６２７
6，６２７
２６２
2)9６２

CriterionB

Consistency(％）
８７４７１
6,874(71.7％）
5,750(868％）
1,124(379％）

ＡＬＬ

Ｃｈａｒ
Ｔ aｇ

Inconsistency(％）
２７１８．３
2,715(283％）
877(132％）
1,838(620％）

ＴＯＴＡＬ
８９
9)５８９
６６２７
6，６２７
２９６２
2，９６２

CriteriomCConsistency(％）
７２９7,294(761％）
１３０９２．５
6,130(925％）
１１４３９．３９７’
1,164(393％）

ＡＬ
ＡＬＬ

Ｃｈａｒ
Ｔaｇ

Inconsistency(％）
2,295(239％）
４９７７．５497(7.5％）
17960.797'
1,798(607％）

CriteriomD

Ｃｈａｒ
Ｔ aｇ

ＡＬ
ＡＬＬ

Ｃｈａｒ
Ｔ aｇ

’
１１

ＡＬ
ＡＬＬ

Consistency(％）
７０７７．４
7,470(77.4％）
６１３０９２．５
6,130(925％）
1,340(452％）

Consistency(％）
６２７７９7,627(795％）
１３０９２．５
6,130(925％）
４９７５０．５
1,497(50.5％）

Inconsistenc y(％）
２１１９２２．１
2,119(221％）
497(7.5％）
1,622(548％）

Criterion:Ｅ
ｌｎｎｃ y(％）
Inconsistenc
１９６２２０．５
1,962(205％）
４９７７．５497(７５％）
1,465(495％）

Ｉ

ＴＯＴＡＬ

’

ＴＯＴＡＬ

’

ＴＯＴＡＬ

９５８９
9，５８９
６６２７
6,627
２９６２
2,962

９５８０
9，５８９
６６２７
６)６２７
２９６２
２，９６２

９５８９
9，５８９
６６２７
6１６２７
２９６２
2，９６２

AcousticAnallysisandnanscriptionofLinguisticanclParalinguisticF1eatures

２１

Table4：Inclusionofcorrespondednonverbalfeaturesbyloosingthecriteria．
ｒｌｅｒｌｏCriterion■

CriterionB

CriterionC
CriterionE
Total

I

Corresponded(％）
176(5.9）216(7.3）
157(5.3）

549(185）

Ｔａｂｌｅ５：Inconsistenciesofsound．
Contents

Ｌｏｎｇｖｏｗｅｌ

aｔｔｈｅｅｎｄｏｆｗｏｒｄ

ininteIjection
lｏｎｇｖｏｗｅｌｉｎｔｈｅｗｏｒｄ
Ｏｖｅｒｌ aｐ
Misleadin ９
Ｄｏｕｂｌｅｃｏｎｓｏｎａｎｔ

aｔｔｈｅｅｎｄｏｆｗｏｒｄ

ininteljection

ｄｏｕｂｌｅｃｏｎｓｏｎａｎｔｉｎｔｈｅｗｏｒｄ

Ｌａｕｇｈ
Ｏｔｈｅｒｓ

’

’’一

Ｔｏｔａｌ

Inconsistencies(％）
【】70(14.1％）26(5.2％）42(8.5％）110(22.1％）27(5.4％）6(1.2％）
7（1.4％）
80(16.1％）)(２６．０％129(26.0％）497(100％）

ＩｎＴａｂｌｅ３Ａ,８７％ofsoundsinHiraganacorresponded・But,ｏｎｌｙ３２％ofTagsandEntities
corresponded、ＩｎＴａｂｌｅ３Ｅ，theloosestcriteria,９２％ofsoundSinHiraganacorresponded，
and５０％ofnonverbalfeaturescorresponded
TheseresultssaytranscriptionsofnonverbalfeaturesareafIbctedbytranscribers，sub-jective
Table4showshowmanynonverbalfeaturescorrespondnewlyincludedineachnew

criterion(B,Ｃ,Ｅ),andthosepercentagesagainstallnonverbalfeatures．

6２Ａｎａｌｙｓｉｓｏｆｉｎｃｏｎｓｉｓｔｅｎｃｉｅｓ
Table5showsinconsistenciesoftranscribedsoundwithHiraganainTaｂｌｅ３Ｅ，，％，，means
eachpercentageagamstwholesoundsinconsistenciesinTable3E・Here’３６％arecausedby
diffbrencesinsoundlength，longvowelsanddoubleconsonants・Manyofotherinconsistenciesarecausedbymisleading．However，therearesoundsthataredifficulttotranscribein
Hiragana,ｅ,3,laughlnprevioussection，weseetheproblemistranscriptionsofnonverbalfeatures・Almost
inconsistencyofnonverbalfeaturesconsistsofomissionsandinsertions・Ｔｈｏｓｅａｒｅｌ,436
itemsagainstl,465itemsofinconsistencyofnonverbalfeatures・The29remaindersare
contradicteditemsbetweentranscriptions・Table6showscontentsofomissionsandinsertions・Here,upperelementsareparalinguisticfbaturesandlowerelementsareconversational

analysisinfOrmation．，，％1”meanspercentagesagainstnumberofeachnonverbalfeature．

’一一一二奉書三警曇震蕾譽藝鑿一墨で疋汀「．、窒号な事・←馨寒ご辱鳶ミミユ皇幸鳶．、寺｝

ShigeyoshiKitazawaetaL

2２

ｎｔＯｍｓｓｌｏｓ８ＺＩｎｓｅｒｔｉｏｎｓ

２６２
２６２

ｊ１１１１１’３３１

１３０９４２

ＴＯＴＡＬ

3４８％％％％％％

SpeakerExchangeⅡ２９８９３６３１１．．：：

Ｐａｕｓｅ．．、９７７

Overlap

船％船％％％

Ｏｔｈｅｒｓ２９１６３６

NonverbalSounds

８８１６了４

Ｌｏｕｄｎｅｓｓ

SpeechRate

旧伍伍くくく`VOiceQuality

１７９蛆蛆的妬Ⅳ妬１

４７４４０２３９２０３３
７３４２１１

Pitch

78(22.4％:5.4％）73(220％:5.1％）8(３．０％:0.6％）1436(48.5％:100％），，％２，，meanspercentageagainstallofomissionsandinsertions・Paralinguisticfbatures,thosearepitch,voicequality,loudnessandspeechrate,marked
highinconsistencyrates・However,utteranceoverlaps，pausesandspeakerexchanges，these
areconversationalanalysisinfbrmation，havelowerinconsistencyratethanparalinguistic
ones・Thosefeatures，unlikeparalinguisticfbatures，ｄｏｎ，thaveanydegree・Thosearejudgedbytranscribers，subjective・

Incaseofprosodytranscription,８０％consistencywasachieved[４１Theresearchused
agraphicalinterface，ｅ､９．，ａｓｐｅｅｃｈｗａvefbrm、Inthispaper，wecouldseeparalinguisticfeatureshadhighinconsistencyrate、Thosefeatures，however，aremeasurableasconcrete

acousticalfeatures・Thismeansthatthosefeaturescouldbeestimatedautomatically,like

inearlierpartofthispaperanｄ[５１Therefbre,ifwepresentsuchinfOrmationgraphicallytotranscribers，thenconsistencyratescouldbecomｅｈｉｇｈｅｒｔｈａｎｉｎｔｈｉｓｐａｐｅｒ・Anotherway,whichwillmakemuchbetterconsistency,istobecomeexpertandtoverifj7
transcription・WemadeaprogramtoverifyTEIbasedtranscriptionltinterpretssomeTEITagandEntities,thenmakessimplesinusoidwavesoundslnterpretableTagsandEntitieｓ
ａｒｅｓｈｏｗｎａｔＴａｂｌｅ７、Speakersaretranscribedwith，uLtagandrepresentedwithpitchof
sounds・Highpitchisassignedtoaspeaker,andlowpitchisassignedtoanother・Overlaps
aretranscribedwith，anchor，-tagandalsointerpretable,theprogramcanoutputoverlappedsound．，Inhale，and1exhale，aretranscribedwith，vocaP-tagandarerepresentedaspause
now、Hence,transcriberscanheartranscribedparalinguisticfbaturesandcanveribown
transcription．

７Ｃｏｎｃｌｕｓｉｏｎ
lnpreliminaryexperimentofspeechrateestimation，theresultsshowavailabilityofthｅ

ｍｅｔｈｏｄｉｎｔｈｉｓｐａｐｅｒｔｏｅｓｔｉｍａｔｅｓｐeechratesautomatically・However，intheexperiment
withrealspeechdatashowedcomplicatedspectrograms・Wewillresearchhowtotrack
speechratesfromacomplicatedspectrogram・Inexperienceofconsistencyofinter-transcribers，transcription，wegotaconsistency
rateofover80％toverbalsoundNonverbalfeatures,however,areaffectedbytranscribers，

subjectiveThen,therearemanyomissionsandinsertions・Wewillexaminechangesof（・台民矛ら．。早；ぞ、ミ、｜ｆ己やご色

Table6：Omissionsandinsertionsatnonverbalfeatures．

AcousticAnalysisandTranscziptionofLinguisticandParalinguisticFeatures

2３

Table7：InterpretableTags＆EntitiesbyVerificationProgram．
Ｔａｇｓ

rｎｅａｎｓaｎｃｈｏｒoverlap

ｐａｕｓｅ

ｐａｕｓｅ

shift

cｈａｎｇｅｓｏｆｌｏｕｄ１ｐｉｔｃｈａｎｄｔｅｍｐｏｕutterance

vocal

inhale､exhole

１lｅｓ
Entities■＆stress；＆trunc；＆lｆrｎｅａｎｓstresstruncated sｙ llable
lowfallintonation＆lr；lowriseintonation＆ｆｒ ）fallriseintonation＆rｆ

risefallintonation●consistencyratecausebytranscribers，skill,agraphicalinterface,andaverifyingprogram．

Ref℃rences[1］GeorgeLTrager：，，Paralanguage:AFirstApproximation，，,StudiesinLinguistics,1３，
１－１１，１９５８．

[2]MasayoKatoh,MitsuoKomura,Shin'ichiroHashimoto：，，RhythmruleinJapanese
basedonthecenterofenergygravityofvowels，，，TheJournaloftheAcousticalSocietyofJapan，ｐｐ、888-896,ｖｏＬ５０ｎｏｌｌ，1994.

[3]OMSperberg-McQueen,LouBurnard：，，BaseTagSetfbrTranscriptionofSpoken
Texts，，，ＴＥＩＰ３ｃｈａｐｔｅｒｌｌ，TextEncodinglnitiative，Chicago，Oxfbrd，1994.

[4]KimSilverman,MaryBeckman,JohnPirelli，MariOstendorf,ColinWightman,Patti
Price，JanetPierrehumbert,JuliaHirschberg:，，ＴＯＢI:AStandardfbrLabelingEnglish
Prosody，，，ＰｒｏｃｏｆｌＣＳＬＰ１９９２ｐｐ、867-870.

に]ShigeyoshiKitazawa,SatoshiKobayashi,TakaoMatsunaga,HideyaIchikawa,，'Tempo
EstimationbyEnvelopefbrRecognitionofParalinguisticFeaturesinSpontaneous
Speech，，，Proc・ｏｆＩＣＳＬＰ１９９４ｐｐｌ６９１－１６９４．