Unanswerable
NONE
telephone calls
EXTRACTIVE
Our simple system (§ SECREF2 ) builds on unsupervised speech processing BIBREF5 , BIBREF6 , BIBREF7 , and in particular on unsupervised term discovery (UTD), which creates hard clusters of repeated word-like units in raw speech BIBREF8 , BIBREF9 . The clusters do not account for all of the audio, but we can use them to simulate a partial, noisy transcription, or pseudotext, which we pair with translations to learn a bag-of-words translation model. We test our system on the CALLHOME Spanish-English speech translation corpus BIBREF10 , a noisy multi-speaker corpus of telephone calls in a variety of Spanish dialects (§ SECREF3 ). Using the Spanish speech as the source and English text translations as the target, we identify several challenges in the use of UTD, including low coverage of audio and difficulty in cross-speaker clustering (§ SECREF4 ). Despite these difficulties, we demonstrate that the system learns to translate some content words (§ SECREF5 ).
1702.03856
Introduction </s> Typ ical Ġspeech - to - text Ġtranslation Ġsystems Ġpipeline Ġautomatic Ġspeech Ġrecognition Ġ( AS R ) Ġand Ġmachine Ġtranslation Ġ( MT ) ĠB IB REF 0 Ġ. ĠBut Ġhigh - quality ĠAS R Ġrequires Ġhundreds Ġof Ġhours Ġof Ġtransc ribed Ġaudio , Ġwhile Ġhigh - quality ĠMT Ġrequires Ġmillions Ġof Ġwords Ġof Ġparallel Ġtext âĢĶ resources Ġavailable Ġfor Ġonly Ġa Ġtiny Ġfraction Ġof Ġthe Ġworld 's Ġestimated Ġ7 , 000 Ġlanguages ĠB IB REF 1 Ġ. ĠNevertheless , Ġthere Ġare Ġimportant Ġlow - resource Ġsettings Ġin Ġwhich Ġeven Ġlimited Ġspeech Ġtranslation Ġwould Ġbe Ġof Ġimmense Ġvalue : Ġdocumentation Ġof Ġendangered Ġlanguages , Ġwhich Ġoften Ġhave Ġno Ġwriting Ġsystem ĠB IB REF 2 Ġ, ĠB IB REF 3 Ġ; Ġand Ġcrisis Ġresponse , Ġfor Ġwhich Ġtext Ġapplications Ġhave Ġproven Ġuseful ĠB IB REF 4 Ġ, Ġbut Ġonly Ġhelp Ġliter ate Ġpopulations . ĠIn Ġthese Ġsettings , Ġtarget Ġtranslations Ġmay Ġbe Ġavailable . ĠFor Ġexample , Ġad Ġhoc Ġtranslations Ġmay Ġbe Ġcollected Ġin Ġsupport Ġof Ġrelief Ġoperations . ĠCan Ġwe Ġdo Ġanything Ġat Ġall Ġwith Ġthis Ġdata ? </s> In Ġthis Ġexplor atory Ġstudy , Ġwe Ġpresent Ġa Ġspeech - to - text Ġtranslation Ġsystem Ġthat Ġlearns Ġdirectly Ġfrom Ġsource Ġaudio Ġand Ġtarget Ġtext Ġpairs , Ġand Ġdoes Ġnot Ġrequire Ġintermediate ĠAS R Ġor ĠMT . ĠOur Ġwork Ġcomple ments Ġseveral Ġlines Ġof Ġrelated Ġrecent Ġwork . ĠFor Ġexample , Ġdu ong 2015 att ention al Ġand Ġan ton ios + chi ang + du ong EM N LP 2016 Ġpresented Ġmodels Ġthat Ġalign Ġaudio Ġto Ġtranslated Ġtext , Ġbut Ġneither Ġused Ġthese Ġmodels Ġto Ġtry Ġto Ġtranslate Ġnew Ġutter ances Ġ( in Ġfact , Ġthe Ġlatter Ġmodel Ġcannot Ġmake Ġsuch Ġpredictions ). Ġber ard + et al n ip sw orks hop 16 Ġdid Ġdevelop Ġa Ġdirect Ġspeech Ġto Ġtranslation Ġsystem , Ġbut Ġpresented Ġresults Ġonly Ġon Ġa Ġcorpus Ġof Ġsynthetic Ġaudio Ġwith Ġa Ġsmall Ġnumber Ġof Ġspeakers . ĠFinally , ĠAdams Ġet Ġal . Ġad ams + et alin ters pe ech 16 , ad ams + et al em nl p 16 Ġtargeted Ġthe Ġsame Ġlow - resource Ġspeech - to - translation Ġtask , Ġbut Ġinstead Ġof Ġworking Ġwith Ġaudio , Ġthey Ġstarted Ġfrom Ġword Ġor Ġphon eme Ġlatt ices . ĠIn Ġprinciple Ġthese Ġcould Ġbe Ġproduced Ġin Ġan Ġun super vised Ġor Ġminim ally - super vised Ġway , Ġbut Ġin Ġpractice Ġthey Ġused Ġsupervised ĠAS R / phone Ġrecognition . ĠAdditionally , Ġtheir Ġevaluation Ġfocused Ġon Ġphone Ġerror Ġrate Ġrather Ġthan Ġtranslation . ĠIn Ġcontrast Ġto Ġthese Ġapproaches , Ġour Ġmethod Ġcan Ġmake Ġtranslation Ġpredictions Ġfor Ġaudio Ġinput Ġnot Ġseen Ġduring Ġtraining , Ġand Ġwe Ġevaluate Ġit Ġon Ġreal Ġmulti - spe aker Ġspeech Ġdata . </s> Our Ġsimple Ġsystem Ġ( Â§ ĠSEC REF 2 Ġ) Ġbuilds Ġon Ġun super vised Ġspeech Ġprocessing ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġ, Ġand Ġin Ġparticular Ġon Ġun super vised Ġterm Ġdiscovery Ġ( UT D ), Ġwhich Ġcreates Ġhard Ġclusters Ġof Ġrepeated Ġword - like Ġunits Ġin Ġraw Ġspeech ĠB IB REF 8 Ġ, ĠB IB REF 9 Ġ. ĠThe Ġclusters Ġdo Ġnot Ġaccount Ġfor Ġall Ġof Ġthe Ġaudio , Ġbut Ġwe Ġcan Ġuse Ġthem Ġto Ġsimulate Ġa Ġpartial , Ġnoisy Ġtranscription , Ġor Ġpseud ote xt , Ġwhich Ġwe Ġpair Ġwith Ġtranslations Ġto Ġlearn Ġa Ġbag - of - words Ġtranslation Ġmodel . ĠWe Ġtest Ġour Ġsystem Ġon Ġthe ĠCALL HOME ĠSpanish - English Ġspeech Ġtranslation Ġcorpus ĠB IB REF 10 Ġ, Ġa Ġnoisy Ġmulti - spe aker Ġcorpus Ġof Ġtelephone Ġcalls Ġin Ġa Ġvariety Ġof ĠSpanish Ġdialect s Ġ( Â§ ĠSEC REF 3 Ġ). ĠUsing Ġthe ĠSpanish Ġspeech Ġas Ġthe Ġsource Ġand ĠEnglish Ġtext Ġtranslations Ġas Ġthe Ġtarget , Ġwe Ġidentify Ġseveral Ġchallenges Ġin Ġthe Ġuse Ġof ĠUT D , Ġincluding Ġlow Ġcoverage Ġof Ġaudio Ġand Ġdifficulty Ġin Ġcross - spe aker Ġclust ering Ġ( Â§ ĠSEC REF 4 Ġ). ĠDespite Ġthese Ġdifficulties , Ġwe Ġdemonstrate Ġthat Ġthe Ġsystem Ġlearns Ġto Ġtranslate Ġsome Ġcontent Ġwords Ġ( Â§ ĠSEC REF 5 Ġ). </s> From Ġun super vised Ġterm Ġdiscovery Ġto Ġdirect Ġspeech - to - text Ġtranslation </s> For ĠUT D Ġwe Ġuse Ġthe ĠZero ĠResource ĠTool kit Ġ( Z RT ools ; ĠJ ansen Ġand ĠVan ĠDur me , Ġ2011 ). ĠZ RT ools Ġuses Ġdynamic Ġtime Ġwar ping Ġ( DT W ) Ġto Ġdiscover Ġpairs Ġof Ġac oust ically Ġsimilar Ġaudio Ġsegments , Ġand Ġthen Ġuses Ġgraph Ġclust ering Ġon Ġoverlapping Ġpairs Ġto Ġform Ġa Ġhard Ġclust ering Ġof Ġthe Ġdiscovered Ġsegments . ĠRepl acing Ġeach Ġdiscovered Ġsegment Ġwith Ġits Ġunique Ġcluster Ġlabel , Ġor Ġpseud ot erm , Ġgives Ġus Ġa Ġpartial , Ġnoisy Ġtranscription , Ġor Ġpseud ote xt Ġ( Fig . ĠFIG REF 4 Ġ). </s> In Ġcreating Ġa Ġtranslation Ġmodel Ġfrom Ġthis Ġdata , Ġwe Ġface Ġa Ġdifficulty Ġthat Ġdoes Ġnot Ġarise Ġin Ġthe Ġparallel Ġtexts Ġthat Ġare Ġnormally Ġused Ġto Ġtrain Ġtranslation Ġmodels : Ġthe Ġpseud ote xt Ġdoes Ġnot Ġrepresent Ġall Ġof Ġthe Ġsource Ġwords , Ġsince Ġthe Ġdiscovered Ġsegments Ġdo Ġnot Ġcover Ġthe Ġfull Ġaudio Ġ( Fig . ĠFIG REF 4 Ġ). ĠHence Ġwe Ġmust Ġnot Ġassume Ġthat Ġour ĠMT Ġmodel Ġcan Ġcompletely Ġrecover Ġthe Ġtranslation Ġof Ġa Ġtest Ġsentence . ĠIn Ġthese Ġconditions , Ġthe Ġlanguage Ġmodeling Ġand Ġordering Ġassumptions Ġof Ġmost ĠMT Ġmodels Ġare Ġunw arranted , Ġso Ġwe Ġinstead Ġuse Ġa Ġsimple Ġbag - of - words Ġtranslation Ġmodel Ġbased Ġonly Ġon Ġco - occ urrence : ĠIBM ĠModel Ġ1 ĠB IB REF 11 Ġwith Ġa ĠDir ich let Ġprior Ġover Ġtranslation Ġdistributions , Ġas Ġlearned Ġby Ġfast _ align ĠB IB REF 12 Ġ. ĠIn Ġparticular , Ġfor Ġeach Ġpseud ot erm , Ġwe Ġlearn Ġa Ġtranslation Ġdistribution Ġover Ġpossible Ġtarget Ġwords . ĠTo Ġtranslate Ġa Ġpseud ot erm Ġin Ġtest Ġdata , Ġwe Ġsimply Ġreturn Ġits Ġhighest - pro b ability Ġtranslation Ġ( or Ġtranslations , Ġas Ġdiscussed Ġin ĠÂ§ ĠSEC REF 5 Ġ). </s> This Ġsetup Ġimplies Ġthat Ġin Ġorder Ġto Ġtranslate , Ġwe Ġmust Ġapply ĠUT D Ġon Ġboth Ġthe Ġtraining Ġand Ġtest Ġaudio . ĠUsing Ġadditional Ġ( not Ġonly Ġtraining ) Ġaudio Ġin ĠUT D Ġincreases Ġthe Ġlikelihood Ġof Ġdiscovering Ġmore Ġclusters . ĠWe Ġtherefore Ġgenerate Ġpseud ote xt Ġfor Ġthe Ġcombined Ġaudio , Ġtrain Ġthe ĠMT Ġmodel Ġon Ġthe Ġpseud ote xt Ġof Ġthe Ġtraining Ġaudio , Ġand Ġapply Ġit Ġto Ġthe Ġpseud ote xt Ġof Ġthe Ġtest Ġdata . ĠThis Ġis Ġfair Ġsince Ġthe ĠUT D Ġhas Ġaccess Ġto Ġonly Ġthe Ġaudio . </s> Dat as et </s> Although Ġwe Ġdid Ġnot Ġhave Ġaccess Ġto Ġa Ġlow - resource Ġdataset , Ġthere Ġis Ġa Ġcorpus Ġof Ġnoisy Ġmulti - spe aker Ġspeech Ġthat Ġsim ulates Ġmany Ġof Ġthe Ġconditions Ġwe Ġexpect Ġto Ġfind Ġin Ġour Ġmotivating Ġapplications : Ġthe ĠCALL HOME ĠSpanish âĢĵ English Ġspeech Ġtranslation Ġdataset Ġ( L DC 2014 T 23 ; ĠPost Ġel Ġal ., Ġ2013 ). ĠWe Ġran ĠUT D Ġover Ġall Ġ104 Ġtelephone Ġcalls , Ġwhich Ġpair Ġ11 Ġhours Ġof Ġaudio Ġwith ĠSpanish Ġtranscripts Ġand Ġtheir Ġcrowds ourced ĠEnglish Ġtranslations . ĠThe Ġtranscripts Ġcontain Ġ168 , 195 ĠSpanish Ġword Ġtokens Ġ( 10 , 674 Ġtypes ), Ġand Ġthe Ġtranslations Ġcontain Ġ159 , 777 ĠEnglish Ġword Ġtokens Ġ( 6 , 7 23 Ġtypes ). ĠThough Ġour Ġsystem Ġdoes Ġnot Ġrequire ĠSpanish Ġtranscripts , Ġwe Ġuse Ġthem Ġto Ġevaluate ĠUT D Ġand Ġto Ġsimulate Ġa Ġperfect ĠUT D Ġsystem , Ġcalled Ġthe Ġor acle . </s> For ĠMT Ġtraining , Ġwe Ġuse Ġthe Ġpseud ote xt Ġand Ġtranslations Ġof Ġ50 Ġcalls , Ġand Ġwe Ġfilter Ġout Ġstop words Ġin Ġthe Ġtranslations Ġwith ĠNL T K ĠB IB REF 15 Ġ. ĠSince ĠUT D Ġis Ġbetter Ġat Ġmatching Ġpatterns Ġfrom Ġthe Ġsame Ġspeaker Ġ( Â§ ĠSEC REF 8 Ġ), Ġwe Ġcreated Ġtwo Ġtypes Ġof Ġ90 / 10 % Ġtrain / test Ġsplit : Ġat Ġthe Ġcall Ġlevel Ġand Ġat Ġthe Ġutter ance Ġlevel . ĠFor Ġthe Ġlatter , Ġ90 % Ġof Ġthe Ġutter ances Ġare Ġrandomly Ġchosen Ġfor Ġthe Ġtraining Ġset Ġ( independent Ġof Ġwhich Ġcall Ġthey Ġoccur Ġin ), Ġand Ġthe Ġrest Ġgo Ġin Ġthe Ġtest Ġset . ĠHence Ġat Ġthe Ġutter ance Ġlevel , Ġbut Ġnot Ġthe Ġcall Ġlevel , Ġsome Ġspeakers Ġare Ġincluded Ġin Ġboth Ġtraining Ġand Ġtest Ġdata . ĠAlthough Ġthe Ġutter ance - level Ġsplit Ġis Ġoptimistic , Ġit Ġallows Ġus Ġto Ġinvestigate Ġhow Ġmultiple Ġspeakers Ġaffect Ġsystem Ġperformance . ĠIn Ġeither Ġcase , Ġthe Ġor acle Ġhas Ġabout Ġ38 k ĠSpanish Ġtokens Ġto Ġtrain Ġon . </s> Analysis Ġof Ġchallenges Ġfrom ĠUT D </s> Our Ġsystem Ġrelies Ġon Ġthe Ġpseud ote xt Ġproduced Ġby ĠZ RT ools Ġ( the Ġonly Ġfreely Ġavailable ĠUT D Ġsystem Ġwe Ġare Ġaware Ġof ), Ġwhich Ġpresents Ġseveral Ġchallenges Ġfor ĠMT . ĠWe Ġused Ġthe Ġdefault ĠZ RT ools Ġparameters , Ġand Ġit Ġmight Ġbe Ġpossible Ġto Ġtune Ġthem Ġto Ġour Ġtask , Ġbut Ġwe Ġleave Ġthis Ġto Ġfuture Ġwork . </s> Ass igning Ġwrong Ġwords Ġto Ġa Ġcluster </s> Since ĠUT D Ġis Ġun super vised , Ġthe Ġdiscovered Ġclusters Ġare Ġnoisy . ĠFig . ĠFIG REF 4 Ġshows Ġan Ġexample Ġof Ġan Ġincorrect Ġmatch Ġbetween Ġthe Ġac oust ically Ġsimilar ĠâĢ ľ qu Ã© Ġtal Ġvas Ġcon âĢ Ŀ Ġand ĠâĢ ľ te Ġtr ab ajo Ġy âĢ Ŀ Ġin Ġutter ances ĠB Ġand ĠC , Ġleading Ġto Ġa Ġcommon Ġassignment Ġto Ġc 2 . ĠSuch Ġinconsistencies Ġin Ġturn Ġaffect Ġthe Ġtranslation Ġdistribution Ġconditioned Ġon Ġc 2 . </s> Many Ġof Ġthese Ġerrors Ġare Ġdue Ġto Ġcross - spe aker Ġmatches , Ġwhich Ġare Ġknown Ġto Ġbe Ġmore Ġchallenging Ġfor ĠUT D ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġ, ĠB IB REF 18 Ġ. ĠMost Ġmatches Ġin Ġour Ġcorpus Ġare Ġacross Ġcalls , Ġyet Ġthese Ġare Ġalso Ġthe Ġleast Ġaccurate Ġ( Table ĠT AB REF 9 Ġ). ĠWithin - utter ance Ġmatches , Ġwhich Ġare Ġalways Ġfrom Ġthe Ġsame Ġspeaker , Ġare Ġthe Ġmost Ġreliable , Ġbut Ġmake Ġup Ġthe Ġsmallest Ġproportion Ġof Ġthe Ġdiscovered Ġpairs . ĠWithin - call Ġmatches Ġfall Ġin Ġbetween . ĠOverall , Ġaverage Ġcluster Ġpurity Ġis Ġonly Ġ34 %, Ġmeaning Ġthat Ġ66 % Ġof Ġdiscovered Ġpatterns Ġdo Ġnot Ġmatch Ġthe Ġmost Ġfrequent Ġtype Ġin Ġtheir Ġcluster . </s> Spl itting Ġwords Ġacross Ġdifferent Ġclusters </s> Although Ġmost ĠUT D Ġmatches Ġare Ġacross Ġspeakers , Ġrecall Ġof Ġcross - spe aker Ġmatches Ġis Ġlower Ġthan Ġfor Ġsame - spe aker Ġmatches . ĠAs Ġa Ġresult , Ġthe Ġsame Ġword Ġfrom Ġdifferent Ġspeakers Ġoften Ġappears Ġin Ġmultiple Ġclusters , Ġpreventing Ġthe Ġmodel Ġfrom Ġlearning Ġgood Ġtranslations . ĠZ RT ools Ġdiscovers Ġ15 , 089 Ġclusters Ġin Ġour Ġdata , Ġthough Ġthere Ġare Ġonly Ġ10 , 674 Ġword Ġtypes . ĠOnly Ġ1 , 614 Ġof Ġthe Ġclusters Ġmap Ġone - to - one Ġto Ġa Ġunique Ġword Ġtype , Ġwhile Ġa Ġmany - to - one Ġmapping Ġof Ġthe Ġrest Ġcovers Ġonly Ġ1 , 8 19 Ġgold Ġtypes Ġ( le aving Ġ7 , 241 Ġgold Ġtypes Ġwith Ġno Ġcorresponding Ġcluster ). </s> Frag mentation Ġof Ġwords Ġacross Ġclusters Ġrenders Ġpseud oter ms Ġimpossible Ġto Ġtranslate Ġwhen Ġthey Ġappear Ġonly Ġin Ġtest Ġand Ġnot Ġin Ġtraining . ĠTable ĠT AB REF 10 Ġshows Ġthat Ġthese Ġpseud ote xt Ġout - of - voc abulary Ġ( OO V ) Ġwords Ġare Ġfrequent , Ġespecially Ġin Ġthe Ġcall - level Ġsplit . ĠThis Ġreflects Ġdifferences Ġin Ġacoustic Ġpatterns Ġof Ġdifferent Ġspeakers , Ġbut Ġalso Ġin Ġtheir Ġvocabulary ĠâĢĶ Ġeven Ġthe Ġor acle ĠO OV Ġrate Ġis Ġhigher Ġin Ġthe Ġcall - level Ġsplit . </s> UT D Ġis Ġsparse , Ġgiving Ġlow Ġcoverage </s> UT D Ġis Ġmost Ġreliable Ġon Ġlong Ġand Ġfrequently - repe ated Ġpatterns , Ġso Ġmany Ġspoken Ġwords Ġare Ġnot Ġrepresented Ġin Ġthe Ġpseud ote xt , Ġas Ġin ĠFig . ĠFIG REF 4 Ġ. ĠWe Ġfound Ġthat Ġthe Ġpatterns Ġdiscovered Ġby ĠZ RT ools Ġmatch Ġonly Ġ28 % Ġof Ġthe Ġaudio . ĠThis Ġlow Ġcoverage Ġreduces Ġtraining Ġdata Ġsize , Ġaffects Ġalignment Ġquality , Ġand Ġadversely Ġaffects Ġtranslation , Ġwhich Ġis Ġonly Ġpossible Ġwhen Ġpseud oter ms Ġare Ġpresent . ĠFor Ġalmost Ġhalf Ġthe Ġutter ances , ĠUT D Ġfails Ġto Ġproduce Ġany Ġpseud ot erm Ġat Ġall .
Introduction Typical speech-to-text translation systems pipeline automatic speech recognition (ASR) and machine translation (MT) BIBREF0 . But high-quality ASR requires hundreds of hours of transcribed audio, while high-quality MT requires millions of words of parallel text—resources available for only a tiny fraction of the world's estimated 7,000 languages BIBREF1 . Nevertheless, there are important low-resource settings in which even limited speech translation would be of immense value: documentation of endangered languages, which often have no writing system BIBREF2 , BIBREF3 ; and crisis response, for which text applications have proven useful BIBREF4 , but only help literate populations. In these settings, target translations may be available. For example, ad hoc translations may be collected in support of relief operations. Can we do anything at all with this data? In this exploratory study, we present a speech-to-text translation system that learns directly from source audio and target text pairs, and does not require intermediate ASR or MT. Our work complements several lines of related recent work. For example, duong2015attentional and antonios+chiang+duongEMNLP2016 presented models that align audio to translated text, but neither used these models to try to translate new utterances (in fact, the latter model cannot make such predictions). berard+etalnipsworkshop16 did develop a direct speech to translation system, but presented results only on a corpus of synthetic audio with a small number of speakers. Finally, Adams et al. adams+etalinterspeech16,adams+etalemnlp16 targeted the same low-resource speech-to-translation task, but instead of working with audio, they started from word or phoneme lattices. In principle these could be produced in an unsupervised or minimally-supervised way, but in practice they used supervised ASR/phone recognition. Additionally, their evaluation focused on phone error rate rather than translation. In contrast to these approaches, our method can make translation predictions for audio input not seen during training, and we evaluate it on real multi-speaker speech data. Our simple system (§ SECREF2 ) builds on unsupervised speech processing BIBREF5 , BIBREF6 , BIBREF7 , and in particular on unsupervised term discovery (UTD), which creates hard clusters of repeated word-like units in raw speech BIBREF8 , BIBREF9 . The clusters do not account for all of the audio, but we can use them to simulate a partial, noisy transcription, or pseudotext, which we pair with translations to learn a bag-of-words translation model. We test our system on the CALLHOME Spanish-English speech translation corpus BIBREF10 , a noisy multi-speaker corpus of telephone calls in a variety of Spanish dialects (§ SECREF3 ). Using the Spanish speech as the source and English text translations as the target, we identify several challenges in the use of UTD, including low coverage of audio and difficulty in cross-speaker clustering (§ SECREF4 ). Despite these difficulties, we demonstrate that the system learns to translate some content words (§ SECREF5 ). From unsupervised term discovery to direct speech-to-text translation For UTD we use the Zero Resource Toolkit (ZRTools; Jansen and Van Durme, 2011). ZRTools uses dynamic time warping (DTW) to discover pairs of acoustically similar audio segments, and then uses graph clustering on overlapping pairs to form a hard clustering of the discovered segments. Replacing each discovered segment with its unique cluster label, or pseudoterm, gives us a partial, noisy transcription, or pseudotext (Fig. FIGREF4 ). In creating a translation model from this data, we face a difficulty that does not arise in the parallel texts that are normally used to train translation models: the pseudotext does not represent all of the source words, since the discovered segments do not cover the full audio (Fig. FIGREF4 ). Hence we must not assume that our MT model can completely recover the translation of a test sentence. In these conditions, the language modeling and ordering assumptions of most MT models are unwarranted, so we instead use a simple bag-of-words translation model based only on co-occurrence: IBM Model 1 BIBREF11 with a Dirichlet prior over translation distributions, as learned by fast_align BIBREF12 . In particular, for each pseudoterm, we learn a translation distribution over possible target words. To translate a pseudoterm in test data, we simply return its highest-probability translation (or translations, as discussed in § SECREF5 ). This setup implies that in order to translate, we must apply UTD on both the training and test audio. Using additional (not only training) audio in UTD increases the likelihood of discovering more clusters. We therefore generate pseudotext for the combined audio, train the MT model on the pseudotext of the training audio, and apply it to the pseudotext of the test data. This is fair since the UTD has access to only the audio. Dataset Although we did not have access to a low-resource dataset, there is a corpus of noisy multi-speaker speech that simulates many of the conditions we expect to find in our motivating applications: the CALLHOME Spanish–English speech translation dataset (LDC2014T23; Post el al., 2013). We ran UTD over all 104 telephone calls, which pair 11 hours of audio with Spanish transcripts and their crowdsourced English translations. The transcripts contain 168,195 Spanish word tokens (10,674 types), and the translations contain 159,777 English word tokens (6,723 types). Though our system does not require Spanish transcripts, we use them to evaluate UTD and to simulate a perfect UTD system, called the oracle. For MT training, we use the pseudotext and translations of 50 calls, and we filter out stopwords in the translations with NLTK BIBREF15 . Since UTD is better at matching patterns from the same speaker (§ SECREF8 ), we created two types of 90/10% train/test split: at the call level and at the utterance level. For the latter, 90% of the utterances are randomly chosen for the training set (independent of which call they occur in), and the rest go in the test set. Hence at the utterance level, but not the call level, some speakers are included in both training and test data. Although the utterance-level split is optimistic, it allows us to investigate how multiple speakers affect system performance. In either case, the oracle has about 38k Spanish tokens to train on. Analysis of challenges from UTD Our system relies on the pseudotext produced by ZRTools (the only freely available UTD system we are aware of), which presents several challenges for MT. We used the default ZRTools parameters, and it might be possible to tune them to our task, but we leave this to future work. Assigning wrong words to a cluster Since UTD is unsupervised, the discovered clusters are noisy. Fig. FIGREF4 shows an example of an incorrect match between the acoustically similar “qué tal vas con” and “te trabajo y” in utterances B and C, leading to a common assignment to c2. Such inconsistencies in turn affect the translation distribution conditioned on c2. Many of these errors are due to cross-speaker matches, which are known to be more challenging for UTD BIBREF16 , BIBREF17 , BIBREF18 . Most matches in our corpus are across calls, yet these are also the least accurate (Table TABREF9 ). Within-utterance matches, which are always from the same speaker, are the most reliable, but make up the smallest proportion of the discovered pairs. Within-call matches fall in between. Overall, average cluster purity is only 34%, meaning that 66% of discovered patterns do not match the most frequent type in their cluster. Splitting words across different clusters Although most UTD matches are across speakers, recall of cross-speaker matches is lower than for same-speaker matches. As a result, the same word from different speakers often appears in multiple clusters, preventing the model from learning good translations. ZRTools discovers 15,089 clusters in our data, though there are only 10,674 word types. Only 1,614 of the clusters map one-to-one to a unique word type, while a many-to-one mapping of the rest covers only 1,819 gold types (leaving 7,241 gold types with no corresponding cluster). Fragmentation of words across clusters renders pseudoterms impossible to translate when they appear only in test and not in training. Table TABREF10 shows that these pseudotext out-of-vocabulary (OOV) words are frequent, especially in the call-level split. This reflects differences in acoustic patterns of different speakers, but also in their vocabulary — even the oracle OOV rate is higher in the call-level split. UTD is sparse, giving low coverage UTD is most reliable on long and frequently-repeated patterns, so many spoken words are not represented in the pseudotext, as in Fig. FIGREF4 . We found that the patterns discovered by ZRTools match only 28% of the audio. This low coverage reduces training data size, affects alignment quality, and adversely affects translation, which is only possible when pseudoterms are present. For almost half the utterances, UTD fails to produce any pseudoterm at all.
what is the domain of the corpus?
8acab64ba72831633e8cc174d5469afecccf3ae9
what Ġis Ġthe Ġdomain Ġof Ġthe Ġcorpus ?
Un answer able
<s> what Ġis Ġthe Ġdomain Ġof Ġthe Ġcorpus ? </s> Introduction </s> Typ ical Ġspeech - to - text Ġtranslation Ġsystems Ġpipeline Ġautomatic Ġspeech Ġrecognition Ġ( AS R ) Ġand Ġmachine Ġtranslation Ġ( MT ) ĠB IB REF 0 Ġ. ĠBut Ġhigh - quality ĠAS R Ġrequires Ġhundreds Ġof Ġhours Ġof Ġtransc ribed Ġaudio , Ġwhile Ġhigh - quality ĠMT Ġrequires Ġmillions Ġof Ġwords Ġof Ġparallel Ġtext âĢĶ resources Ġavailable Ġfor Ġonly Ġa Ġtiny Ġfraction Ġof Ġthe Ġworld 's Ġestimated Ġ7 , 000 Ġlanguages ĠB IB REF 1 Ġ. ĠNevertheless , Ġthere Ġare Ġimportant Ġlow - resource Ġsettings Ġin Ġwhich Ġeven Ġlimited Ġspeech Ġtranslation Ġwould Ġbe Ġof Ġimmense Ġvalue : Ġdocumentation Ġof Ġendangered Ġlanguages , Ġwhich Ġoften Ġhave Ġno Ġwriting Ġsystem ĠB IB REF 2 Ġ, ĠB IB REF 3 Ġ; Ġand Ġcrisis Ġresponse , Ġfor Ġwhich Ġtext Ġapplications Ġhave Ġproven Ġuseful ĠB IB REF 4 Ġ, Ġbut Ġonly Ġhelp Ġliter ate Ġpopulations . ĠIn Ġthese Ġsettings , Ġtarget Ġtranslations Ġmay Ġbe Ġavailable . ĠFor Ġexample , Ġad Ġhoc Ġtranslations Ġmay Ġbe Ġcollected Ġin Ġsupport Ġof Ġrelief Ġoperations . ĠCan Ġwe Ġdo Ġanything Ġat Ġall Ġwith Ġthis Ġdata ? </s> In Ġthis Ġexplor atory Ġstudy , Ġwe Ġpresent Ġa Ġspeech - to - text Ġtranslation Ġsystem Ġthat Ġlearns Ġdirectly Ġfrom Ġsource Ġaudio Ġand Ġtarget Ġtext Ġpairs , Ġand Ġdoes Ġnot Ġrequire Ġintermediate ĠAS R Ġor ĠMT . ĠOur Ġwork Ġcomple ments Ġseveral Ġlines Ġof Ġrelated Ġrecent Ġwork . ĠFor Ġexample , Ġdu ong 2015 att ention al Ġand Ġan ton ios + chi ang + du ong EM N LP 2016 Ġpresented Ġmodels Ġthat Ġalign Ġaudio Ġto Ġtranslated Ġtext , Ġbut Ġneither Ġused Ġthese Ġmodels Ġto Ġtry Ġto Ġtranslate Ġnew Ġutter ances Ġ( in Ġfact , Ġthe Ġlatter Ġmodel Ġcannot Ġmake Ġsuch Ġpredictions ). Ġber ard + et al n ip sw orks hop 16 Ġdid Ġdevelop Ġa Ġdirect Ġspeech Ġto Ġtranslation Ġsystem , Ġbut Ġpresented Ġresults Ġonly Ġon Ġa Ġcorpus Ġof Ġsynthetic Ġaudio Ġwith Ġa Ġsmall Ġnumber Ġof Ġspeakers . ĠFinally , ĠAdams Ġet Ġal . Ġad ams + et alin ters pe ech 16 , ad ams + et al em nl p 16 Ġtargeted Ġthe Ġsame Ġlow - resource Ġspeech - to - translation Ġtask , Ġbut Ġinstead Ġof Ġworking Ġwith Ġaudio , Ġthey Ġstarted Ġfrom Ġword Ġor Ġphon eme Ġlatt ices . ĠIn Ġprinciple Ġthese Ġcould Ġbe Ġproduced Ġin Ġan Ġun super vised Ġor Ġminim ally - super vised Ġway , Ġbut Ġin Ġpractice Ġthey Ġused Ġsupervised ĠAS R / phone Ġrecognition . ĠAdditionally , Ġtheir Ġevaluation Ġfocused Ġon Ġphone Ġerror Ġrate Ġrather Ġthan Ġtranslation . ĠIn Ġcontrast Ġto Ġthese Ġapproaches , Ġour Ġmethod Ġcan Ġmake Ġtranslation Ġpredictions Ġfor Ġaudio Ġinput Ġnot Ġseen Ġduring Ġtraining , Ġand Ġwe Ġevaluate Ġit Ġon Ġreal Ġmulti - spe aker Ġspeech Ġdata . </s> Our Ġsimple Ġsystem Ġ( Â§ ĠSEC REF 2 Ġ) Ġbuilds Ġon Ġun super vised Ġspeech Ġprocessing ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ, ĠB IB REF 7 Ġ, Ġand Ġin Ġparticular Ġon Ġun super vised Ġterm Ġdiscovery Ġ( UT D ), Ġwhich Ġcreates Ġhard Ġclusters Ġof Ġrepeated Ġword - like Ġunits Ġin Ġraw Ġspeech ĠB IB REF 8 Ġ, ĠB IB REF 9 Ġ. ĠThe Ġclusters Ġdo Ġnot Ġaccount Ġfor Ġall Ġof Ġthe Ġaudio , Ġbut Ġwe Ġcan Ġuse Ġthem Ġto Ġsimulate Ġa Ġpartial , Ġnoisy Ġtranscription , Ġor Ġpseud ote xt , Ġwhich Ġwe Ġpair Ġwith Ġtranslations Ġto Ġlearn Ġa Ġbag - of - words Ġtranslation Ġmodel . ĠWe Ġtest Ġour Ġsystem Ġon Ġthe ĠCALL HOME ĠSpanish - English Ġspeech Ġtranslation Ġcorpus ĠB IB REF 10 Ġ, Ġa Ġnoisy Ġmulti - spe aker Ġcorpus Ġof Ġtelephone Ġcalls Ġin Ġa Ġvariety Ġof ĠSpanish Ġdialect s Ġ( Â§ ĠSEC REF 3 Ġ). ĠUsing Ġthe ĠSpanish Ġspeech Ġas Ġthe Ġsource Ġand ĠEnglish Ġtext Ġtranslations Ġas Ġthe Ġtarget , Ġwe Ġidentify Ġseveral Ġchallenges Ġin Ġthe Ġuse Ġof ĠUT D , Ġincluding Ġlow Ġcoverage Ġof Ġaudio Ġand Ġdifficulty Ġin Ġcross - spe aker Ġclust ering Ġ( Â§ ĠSEC REF 4 Ġ). ĠDespite Ġthese Ġdifficulties , Ġwe Ġdemonstrate Ġthat Ġthe Ġsystem Ġlearns Ġto Ġtranslate Ġsome Ġcontent Ġwords Ġ( Â§ ĠSEC REF 5 Ġ). </s> From Ġun super vised Ġterm Ġdiscovery Ġto Ġdirect Ġspeech - to - text Ġtranslation </s> For ĠUT D Ġwe Ġuse Ġthe ĠZero ĠResource ĠTool kit Ġ( Z RT ools ; ĠJ ansen Ġand ĠVan ĠDur me , Ġ2011 ). ĠZ RT ools Ġuses Ġdynamic Ġtime Ġwar ping Ġ( DT W ) Ġto Ġdiscover Ġpairs Ġof Ġac oust ically Ġsimilar Ġaudio Ġsegments , Ġand Ġthen Ġuses Ġgraph Ġclust ering Ġon Ġoverlapping Ġpairs Ġto Ġform Ġa Ġhard Ġclust ering Ġof Ġthe Ġdiscovered Ġsegments . ĠRepl acing Ġeach Ġdiscovered Ġsegment Ġwith Ġits Ġunique Ġcluster Ġlabel , Ġor Ġpseud ot erm , Ġgives Ġus Ġa Ġpartial , Ġnoisy Ġtranscription , Ġor Ġpseud ote xt Ġ( Fig . ĠFIG REF 4 Ġ). </s> In Ġcreating Ġa Ġtranslation Ġmodel Ġfrom Ġthis Ġdata , Ġwe Ġface Ġa Ġdifficulty Ġthat Ġdoes Ġnot Ġarise Ġin Ġthe Ġparallel Ġtexts Ġthat Ġare Ġnormally Ġused Ġto Ġtrain Ġtranslation Ġmodels : Ġthe Ġpseud ote xt Ġdoes Ġnot Ġrepresent Ġall Ġof Ġthe Ġsource Ġwords , Ġsince Ġthe Ġdiscovered Ġsegments Ġdo Ġnot Ġcover Ġthe Ġfull Ġaudio Ġ( Fig . ĠFIG REF 4 Ġ). ĠHence Ġwe Ġmust Ġnot Ġassume Ġthat Ġour ĠMT Ġmodel Ġcan Ġcompletely Ġrecover Ġthe Ġtranslation Ġof Ġa Ġtest Ġsentence . ĠIn Ġthese Ġconditions , Ġthe Ġlanguage Ġmodeling Ġand Ġordering Ġassumptions Ġof Ġmost ĠMT Ġmodels Ġare Ġunw arranted , Ġso Ġwe Ġinstead Ġuse Ġa Ġsimple Ġbag - of - words Ġtranslation Ġmodel Ġbased Ġonly Ġon Ġco - occ urrence : ĠIBM ĠModel Ġ1 ĠB IB REF 11 Ġwith Ġa ĠDir ich let Ġprior Ġover Ġtranslation Ġdistributions , Ġas Ġlearned Ġby Ġfast _ align ĠB IB REF 12 Ġ. ĠIn Ġparticular , Ġfor Ġeach Ġpseud ot erm , Ġwe Ġlearn Ġa Ġtranslation Ġdistribution Ġover Ġpossible Ġtarget Ġwords . ĠTo Ġtranslate Ġa Ġpseud ot erm Ġin Ġtest Ġdata , Ġwe Ġsimply Ġreturn Ġits Ġhighest - pro b ability Ġtranslation Ġ( or Ġtranslations , Ġas Ġdiscussed Ġin ĠÂ§ ĠSEC REF 5 Ġ). </s> This Ġsetup Ġimplies Ġthat Ġin Ġorder Ġto Ġtranslate , Ġwe Ġmust Ġapply ĠUT D Ġon Ġboth Ġthe Ġtraining Ġand Ġtest Ġaudio . ĠUsing Ġadditional Ġ( not Ġonly Ġtraining ) Ġaudio Ġin ĠUT D Ġincreases Ġthe Ġlikelihood Ġof Ġdiscovering Ġmore Ġclusters . ĠWe Ġtherefore Ġgenerate Ġpseud ote xt Ġfor Ġthe Ġcombined Ġaudio , Ġtrain Ġthe ĠMT Ġmodel Ġon Ġthe Ġpseud ote xt Ġof Ġthe Ġtraining Ġaudio , Ġand Ġapply Ġit Ġto Ġthe Ġpseud ote xt Ġof Ġthe Ġtest Ġdata . ĠThis Ġis Ġfair Ġsince Ġthe ĠUT D Ġhas Ġaccess Ġto Ġonly Ġthe Ġaudio . </s> Dat as et </s> Although Ġwe Ġdid Ġnot Ġhave Ġaccess Ġto Ġa Ġlow - resource Ġdataset , Ġthere Ġis Ġa Ġcorpus Ġof Ġnoisy Ġmulti - spe aker Ġspeech Ġthat Ġsim ulates Ġmany Ġof Ġthe Ġconditions Ġwe Ġexpect Ġto Ġfind Ġin Ġour Ġmotivating Ġapplications : Ġthe ĠCALL HOME ĠSpanish âĢĵ English Ġspeech Ġtranslation Ġdataset Ġ( L DC 2014 T 23 ; ĠPost Ġel Ġal ., Ġ2013 ). ĠWe Ġran ĠUT D Ġover Ġall Ġ104 Ġtelephone Ġcalls , Ġwhich Ġpair Ġ11 Ġhours Ġof Ġaudio Ġwith ĠSpanish Ġtranscripts Ġand Ġtheir Ġcrowds ourced ĠEnglish Ġtranslations . ĠThe Ġtranscripts Ġcontain Ġ168 , 195 ĠSpanish Ġword Ġtokens Ġ( 10 , 674 Ġtypes ), Ġand Ġthe Ġtranslations Ġcontain Ġ159 , 777 ĠEnglish Ġword Ġtokens Ġ( 6 , 7 23 Ġtypes ). ĠThough Ġour Ġsystem Ġdoes Ġnot Ġrequire ĠSpanish Ġtranscripts , Ġwe Ġuse Ġthem Ġto Ġevaluate ĠUT D Ġand Ġto Ġsimulate Ġa Ġperfect ĠUT D Ġsystem , Ġcalled Ġthe Ġor acle . </s> For ĠMT Ġtraining , Ġwe Ġuse Ġthe Ġpseud ote xt Ġand Ġtranslations Ġof Ġ50 Ġcalls , Ġand Ġwe Ġfilter Ġout Ġstop words Ġin Ġthe Ġtranslations Ġwith ĠNL T K ĠB IB REF 15 Ġ. ĠSince ĠUT D Ġis Ġbetter Ġat Ġmatching Ġpatterns Ġfrom Ġthe Ġsame Ġspeaker Ġ( Â§ ĠSEC REF 8 Ġ), Ġwe Ġcreated Ġtwo Ġtypes Ġof Ġ90 / 10 % Ġtrain / test Ġsplit : Ġat Ġthe Ġcall Ġlevel Ġand Ġat Ġthe Ġutter ance Ġlevel . ĠFor Ġthe Ġlatter , Ġ90 % Ġof Ġthe Ġutter ances Ġare Ġrandomly Ġchosen Ġfor Ġthe Ġtraining Ġset Ġ( independent Ġof Ġwhich Ġcall Ġthey Ġoccur Ġin ), Ġand Ġthe Ġrest Ġgo Ġin Ġthe Ġtest Ġset . ĠHence Ġat Ġthe Ġutter ance Ġlevel , Ġbut Ġnot Ġthe Ġcall Ġlevel , Ġsome Ġspeakers Ġare Ġincluded Ġin Ġboth Ġtraining Ġand Ġtest Ġdata . ĠAlthough Ġthe Ġutter ance - level Ġsplit Ġis Ġoptimistic , Ġit Ġallows Ġus Ġto Ġinvestigate Ġhow Ġmultiple Ġspeakers Ġaffect Ġsystem Ġperformance . ĠIn Ġeither Ġcase , Ġthe Ġor acle Ġhas Ġabout Ġ38 k ĠSpanish Ġtokens Ġto Ġtrain Ġon . </s> Analysis Ġof Ġchallenges Ġfrom ĠUT D </s> Our Ġsystem Ġrelies Ġon Ġthe Ġpseud ote xt Ġproduced Ġby ĠZ RT ools Ġ( the Ġonly Ġfreely Ġavailable ĠUT D Ġsystem Ġwe Ġare Ġaware Ġof ), Ġwhich Ġpresents Ġseveral Ġchallenges Ġfor ĠMT . ĠWe Ġused Ġthe Ġdefault ĠZ RT ools Ġparameters , Ġand Ġit Ġmight Ġbe Ġpossible Ġto Ġtune Ġthem Ġto Ġour Ġtask , Ġbut Ġwe Ġleave Ġthis Ġto Ġfuture Ġwork . </s> Ass igning Ġwrong Ġwords Ġto Ġa Ġcluster </s> Since ĠUT D Ġis Ġun super vised , Ġthe Ġdiscovered Ġclusters Ġare Ġnoisy . ĠFig . ĠFIG REF 4 Ġshows Ġan Ġexample Ġof Ġan Ġincorrect Ġmatch Ġbetween Ġthe Ġac oust ically Ġsimilar ĠâĢ ľ qu Ã© Ġtal Ġvas Ġcon âĢ Ŀ Ġand ĠâĢ ľ te Ġtr ab ajo Ġy âĢ Ŀ Ġin Ġutter ances ĠB Ġand ĠC , Ġleading Ġto Ġa Ġcommon Ġassignment Ġto Ġc 2 . ĠSuch Ġinconsistencies Ġin Ġturn Ġaffect Ġthe Ġtranslation Ġdistribution Ġconditioned Ġon Ġc 2 . </s> Many Ġof Ġthese Ġerrors Ġare Ġdue Ġto Ġcross - spe aker Ġmatches , Ġwhich Ġare Ġknown Ġto Ġbe Ġmore Ġchallenging Ġfor ĠUT D ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġ, ĠB IB REF 18 Ġ. ĠMost Ġmatches Ġin Ġour Ġcorpus Ġare Ġacross Ġcalls , Ġyet Ġthese Ġare Ġalso Ġthe Ġleast Ġaccurate Ġ( Table ĠT AB REF 9 Ġ). ĠWithin - utter ance Ġmatches , Ġwhich Ġare Ġalways Ġfrom Ġthe Ġsame Ġspeaker , Ġare Ġthe Ġmost Ġreliable , Ġbut Ġmake Ġup Ġthe Ġsmallest Ġproportion Ġof Ġthe Ġdiscovered Ġpairs . ĠWithin - call Ġmatches Ġfall Ġin Ġbetween . ĠOverall , Ġaverage Ġcluster Ġpurity Ġis Ġonly Ġ34 %, Ġmeaning Ġthat Ġ66 % Ġof Ġdiscovered Ġpatterns Ġdo Ġnot Ġmatch Ġthe Ġmost Ġfrequent Ġtype Ġin Ġtheir Ġcluster . </s> Spl itting Ġwords Ġacross Ġdifferent Ġclusters </s> Although Ġmost ĠUT D Ġmatches Ġare Ġacross Ġspeakers , Ġrecall Ġof Ġcross - spe aker Ġmatches Ġis Ġlower Ġthan Ġfor Ġsame - spe aker Ġmatches . ĠAs Ġa Ġresult , Ġthe Ġsame Ġword Ġfrom Ġdifferent Ġspeakers Ġoften Ġappears Ġin Ġmultiple Ġclusters , Ġpreventing Ġthe Ġmodel Ġfrom Ġlearning Ġgood Ġtranslations . ĠZ RT ools Ġdiscovers Ġ15 , 089 Ġclusters Ġin Ġour Ġdata , Ġthough Ġthere Ġare Ġonly Ġ10 , 674 Ġword Ġtypes . ĠOnly Ġ1 , 614 Ġof Ġthe Ġclusters Ġmap Ġone - to - one Ġto Ġa Ġunique Ġword Ġtype , Ġwhile Ġa Ġmany - to - one Ġmapping Ġof Ġthe Ġrest Ġcovers Ġonly Ġ1 , 8 19 Ġgold Ġtypes Ġ( le aving Ġ7 , 241 Ġgold Ġtypes Ġwith Ġno Ġcorresponding Ġcluster ). </s> Frag mentation Ġof Ġwords Ġacross Ġclusters Ġrenders Ġpseud oter ms Ġimpossible Ġto Ġtranslate Ġwhen Ġthey Ġappear Ġonly Ġin Ġtest Ġand Ġnot Ġin Ġtraining . ĠTable ĠT AB REF 10 Ġshows Ġthat Ġthese Ġpseud ote xt Ġout - of - voc abulary Ġ( OO V ) Ġwords Ġare Ġfrequent , Ġespecially Ġin Ġthe Ġcall - level Ġsplit . ĠThis Ġreflects Ġdifferences Ġin Ġacoustic Ġpatterns Ġof Ġdifferent Ġspeakers , Ġbut Ġalso Ġin Ġtheir Ġvocabulary ĠâĢĶ Ġeven Ġthe Ġor acle ĠO OV Ġrate Ġis Ġhigher Ġin Ġthe Ġcall - level Ġsplit . </s> UT D Ġis Ġsparse , Ġgiving Ġlow Ġcoverage </s> UT D Ġis Ġmost Ġreliable Ġon Ġlong Ġand Ġfrequently - repe ated Ġpatterns , Ġso Ġmany Ġspoken Ġwords Ġare Ġnot Ġrepresented Ġin Ġthe Ġpseud ote xt , Ġas Ġin ĠFig . ĠFIG REF 4 Ġ. ĠWe Ġfound Ġthat Ġthe Ġpatterns Ġdiscovered Ġby ĠZ RT ools Ġmatch Ġonly Ġ28 % Ġof Ġthe Ġaudio . ĠThis Ġlow Ġcoverage Ġreduces Ġtraining Ġdata Ġsize , Ġaffects Ġalignment Ġquality , Ġand Ġadversely Ġaffects Ġtranslation , Ġwhich Ġis Ġonly Ġpossible Ġwhen Ġpseud oter ms Ġare Ġpresent . ĠFor Ġalmost Ġhalf Ġthe Ġutter ances , ĠUT D Ġfails Ġto Ġproduce Ġany Ġpseud ot erm Ġat Ġall .