state-of-the-art Transformer architecture, Kaldi, speech clustergen statistical speech synthesizer
EXTRACTIVE
For speech synthesis, they build a speech clustergen statistical speech synthesizer BIBREF9. For speech recognition, they use Kaldi BIBREF11. For Machine Translation, they use a Transformer architecture from BIBREF15.
ABSTRACTIVE
Baseline Results ::: Speech Synthesis In our previous work on building speech systems on found data in 700 languages, BIBREF7, we addressed alignment issues (when audio is not segmented into turn/sentence sized chunks) and correctness issues (when the audio does not match the transcription). We used the same techniques here, as described above. For the best quality speech synthesis we need a few hours of phonetically-balanced, single-speaker, read speech. Our first step was to use the start and end points for each turn in the dialogues, and select those of the most frequent speaker, nmlch. This gave us around 18250 segments. We further automatically removed excessive silence from the start, middle and end of these turns (based on occurrence of F0). This gave us 13 hours and 48 minutes of speech. We phonetically aligned this data and built a speech clustergen statistical speech synthesizer BIBREF9 from all of this data. We resynthesized all of the data and measured the difference between the synthesized data and the original data using Mel Cepstral Distortion, a standard method for automatically measuring quality of speech generation BIBREF10. We then ordered the segments by their generation score and took the top 2000 turns to build a new synthesizer, assuming the better scores corresponded to better alignments, following the techniques of BIBREF7. For speech recognition (ASR) we used Kaldi BIBREF11. As we do not have access to pronunciation lexica for Mapudungun, we had to approximate them with two settings. In the first setting, we make the simple assumption that each character corresponds to a pronunced phoneme. In the second setting, we instead used the generated phonetic lexicon also used in the above-mentioned speech synthesis techniques. The train/dev/test splits are across conversations, as described above. We built neural end-to-end machine translation systems between Mapudungun and Spanish in both directions, using state-of-the-art Transformer architecture BIBREF14 with the toolkit of BIBREF15. We train our systems at the subword level using Byte-Pair Encoding BIBREF16 with a vocabulary of 5000 subwords, shared between the source and target languages. We use five layers for each of the encoder and the decoder, an embedding size of 512, feed forward transformation size of 2048, and eight attention heads. We use dropout BIBREF17 with $0.4$ probability as well as label smoothing set to $0.1$. We train with the Adam optimizer BIBREF18 for up to 200 epochs using learning decay with a patience of six epochs.
We phonetically aligned this data and built a speech clustergen statistical speech synthesizer BIBREF9 from all of this data. We resynthesized all of the data and measured the difference between the synthesized data and the original data using Mel Cepstral Distortion, a standard method for automatically measuring quality of speech generation BIBREF10. We then ordered the segments by their generation score and took the top 2000 turns to build a new synthesizer, assuming the better scores corresponded to better alignments, following the techniques of BIBREF7. For speech recognition (ASR) we used Kaldi BIBREF11. As we do not have access to pronunciation lexica for Mapudungun, we had to approximate them with two settings. In the first setting, we make the simple assumption that each character corresponds to a pronunced phoneme. In the second setting, we instead used the generated phonetic lexicon also used in the above-mentioned speech synthesis techniques. The train/dev/test splits are across conversations, as described above. We built neural end-to-end machine translation systems between Mapudungun and Spanish in both directions, using state-of-the-art Transformer architecture BIBREF14 with the toolkit of BIBREF15. We train our systems at the subword level using Byte-Pair Encoding BIBREF16 with a vocabulary of 5000 subwords, shared between the source and target languages. We use five layers for each of the encoder and the decoder, an embedding size of 512, feed forward transformation size of 2048, and eight attention heads. We use dropout BIBREF17 with $0.4$ probability as well as label smoothing set to $0.1$. We train with the Adam optimizer BIBREF18 for up to 200 epochs using learning decay with a patience of six epochs.
1912.01772
Introduction </s> Recent Ġyears Ġhave Ġseen Ġunprecedented Ġprogress Ġfor ĠNatural ĠLanguage ĠProcessing Ġ( N LP ) Ġon Ġalmost Ġevery ĠN LP Ġsubt ask . ĠEven Ġthough Ġlow - resource Ġsettings Ġhave Ġalso Ġbeen Ġexplored , Ġthis Ġprogress Ġhas Ġoverwhelmingly Ġbeen Ġobserved Ġin Ġlanguages Ġwith Ġsignificant Ġdata Ġresources Ġthat Ġcan Ġbe Ġlever aged Ġto Ġtrain Ġdeep Ġneural Ġnetworks . ĠLow - resource Ġlanguages Ġstill Ġlag Ġbehind . </s> End angered Ġlanguages Ġpose Ġan Ġadditional Ġchallenge . ĠThe Ġprocess Ġof Ġdocumenting Ġan Ġendangered Ġlanguage Ġtypically Ġincludes Ġthe Ġcreation Ġof Ġword Ġlists , Ġaudio Ġand Ġvideo Ġrecordings , Ġnotes , Ġor Ġgrammar Ġfragments , Ġwith Ġthe Ġcreated Ġresources Ġthen Ġstored Ġin Ġlarge Ġonline Ġlingu istics Ġarchives . ĠThis Ġprocess Ġis Ġoften Ġhindered Ġby Ġthe ĠTrans cription ĠBott leneck : Ġthe Ġlinguistic Ġfield worker Ġand Ġthe Ġlanguage Ġcommunity Ġmay Ġnot Ġhave Ġtime Ġto Ġtransc ribe Ġall Ġof Ġthe Ġrecordings Ġand Ġmay Ġonly Ġtransc ribe Ġsegments Ġthat Ġare Ġlingu istically Ġsalient Ġfor Ġpublication Ġor Ġculturally Ġsignificant Ġfor Ġthe Ġcreation Ġof Ġcommunity Ġresources . </s> With Ġthis Ġwork Ġwe Ġmake Ġpublicly Ġavailable Ġa Ġlarge Ġcorpus Ġin ĠMap ud ung un , Ġa Ġlanguage Ġof Ġthe Ġindigenous ĠMap uc he Ġpeople Ġof Ġsouthern ĠChile Ġand Ġwestern ĠArgentina . ĠWe Ġhope Ġto Ġam el ior ate Ġthe Ġresource Ġgap Ġand Ġthe Ġtranscription Ġbottleneck Ġin Ġtwo Ġways . ĠFirst , Ġwe Ġare Ġproviding Ġa Ġlarger Ġdata Ġset Ġthan Ġhas Ġpreviously Ġbeen Ġavailable , Ġand Ġsecond , Ġwe Ġare Ġproviding Ġbas elines Ġfor ĠN LP Ġtasks Ġ( speech Ġrecognition , Ġspeech Ġsynthesis , Ġand Ġmachine Ġtranslation ). ĠIn Ġproviding Ġbas elines Ġand Ġdatasets Ġsplits , Ġwe Ġhope Ġto Ġfurther Ġfacilitate Ġresearch Ġon Ġlow - resource ĠN LP Ġfor Ġthis Ġlanguage Ġthrough Ġour Ġdata Ġset . ĠResearch Ġon Ġlow - resource Ġspeech Ġrecognition Ġis Ġparticularly Ġimportant Ġin Ġrelie ving Ġthe Ġtranscription Ġbottleneck , Ġwhile Ġtackling Ġthe Ġresearch Ġchallenges Ġthat Ġspeech Ġsynthesis Ġand Ġmachine Ġtranslation Ġpose Ġfor Ġsuch Ġlanguages Ġcould Ġlead Ġto Ġsuch Ġsystems Ġbeing Ġdeployed Ġto Ġserve Ġmore Ġunder - represented Ġcommunities . </s> The ĠMap ud ung un ĠLanguage </s> Map ud ung un Ġ( iso Ġ6 39 - 3 : Ġar n ) Ġis Ġan Ġindigenous Ġlanguage Ġof Ġthe ĠAmericas Ġspoken Ġnative ly Ġin ĠChile Ġand ĠArgentina , Ġwith Ġan Ġestimated Ġ100 Ġto Ġ200 Ġthousand Ġspeakers Ġin ĠChile Ġand Ġ27 Ġto Ġ60 Ġthousand Ġspeakers Ġin ĠArgentina ĠB IB REF 0 . ĠIt Ġis Ġan Ġisolate Ġlanguage Ġand Ġis Ġclassified Ġas Ġthreatened Ġby ĠEth n ologue , Ġhence Ġthe Ġcritical Ġimportance Ġof Ġall Ġdocumentary Ġefforts . ĠAlthough Ġthe Ġmorphology Ġof Ġnoun s Ġis Ġrelatively Ġsimple , ĠMap ud ung un Ġverb Ġmorphology Ġis Ġhighly Ġagg l ut in ative Ġand Ġcomplex . ĠSome Ġanalyses Ġprovide Ġas Ġmany Ġas Ġ36 Ġverb Ġsuffix Ġslots ĠB IB REF 1 . ĠA Ġtypical Ġcomplex Ġverb Ġform Ġoccurring Ġin Ġour Ġcorpus Ġof Ġspoken ĠMap ud ung un Ġconsists Ġof Ġfive Ġor Ġsix Ġmorp hem es . </s> Map ud ung un Ġhas Ġseveral Ġinteresting Ġgram matical Ġproperties . ĠIt Ġis Ġa Ġpoly sy nt hetic Ġlanguage Ġin Ġthe Ġsense Ġof ĠB IB REF 2 ; Ġsee ĠB IB REF 3 Ġfor Ġexplicit Ġargument ation . ĠAs Ġwith Ġother Ġpoly sy nt hetic Ġlanguages , ĠMap ud ung un Ġhas ĠN oun ĠInc orpor ation ; Ġhowever , Ġit Ġis Ġunique Ġinsofar Ġas Ġthe ĠN oun Ġappears Ġto Ġthe Ġright Ġof Ġthe ĠVerb , Ġinstead Ġof Ġto Ġthe Ġleft , Ġas Ġin Ġmost Ġpoly sy nt hetic Ġlanguages ĠB IB REF 4 . ĠOne Ġfurther Ġdistinction Ġof ĠMap ud ung un Ġis Ġthat , Ġwhereas Ġother Ġpoly sy nt hetic Ġlanguages Ġare Ġcharacterized Ġby Ġa Ġlack Ġof Ġinf init ives , ĠMap ud ung un Ġhas Ġinf init ival Ġverb Ġforms ; Ġthat Ġis , Ġwhile Ġsubordinate Ġclauses Ġin ĠMap ud ung un Ġclosely Ġresemble Ġpossessed Ġnom inals Ġand Ġmay Ġoccur Ġwith Ġan Ġanalytic Ġmarker Ġresembling Ġposs essor Ġagreement , Ġthere Ġis Ġno Ġagreement Ġinf lection Ġon Ġthe Ġverb Ġitself . ĠOne Ġfurther Ġremarkable Ġproperty Ġof ĠMap ud ung un Ġis Ġits Ġinverse Ġvoice Ġsystem Ġof Ġagreement , Ġwhereby Ġthe Ġhighest Ġagreement Ġis Ġwith Ġthe Ġargument Ġhighest Ġin Ġan Ġanim acy Ġhierarchy Ġregardless Ġof Ġthem atic Ġrole ĠB IB REF 5 . </s> The ĠResource </s> The Ġresource Ġis Ġcomprised Ġof Ġ142 Ġhours Ġof Ġspoken ĠMap ud ung un Ġthat Ġwas Ġrecorded Ġduring Ġthe ĠAV EN UE Ġproject ĠB IB REF 6 Ġin Ġ2001 Ġto Ġ2005 . ĠThe Ġdata Ġwas Ġrecorded Ġunder Ġa Ġpartnership Ġbetween Ġthe ĠAV EN UE Ġproject , Ġfunded Ġby Ġthe ĠUS ĠNational ĠScience ĠFoundation Ġat ĠCarnegie ĠMellon ĠUniversity , Ġthe ĠChilean ĠMinistry Ġof ĠEducation Ġ( M ined uc ), Ġand Ġthe ĠInstit uto Ġde ĠEst ud ios ĠInd ÃŃ gen as Ġat ĠUnivers idad Ġde ĠLa ĠFr on tera , Ġoriginally Ġspanning Ġ170 Ġhours Ġof Ġaudio . ĠWe Ġhave Ġrecently Ġcleaned Ġthe Ġdata Ġand Ġare Ġreleasing Ġit Ġpublicly Ġfor Ġthe Ġfirst Ġtime Ġ( although Ġit Ġhas Ġbeen Ġshared Ġwith Ġindividual Ġresearchers Ġin Ġthe Ġpast ) Ġalong Ġwith ĠN LP Ġbas elines . </s> The Ġrecordings Ġwere Ġtransc ribed Ġand Ġtranslated Ġinto ĠSpanish Ġat Ġthe ĠInstit uto Ġde ĠEst ud ios ĠInd ÃŃ gen as Ġat ĠUnivers idad Ġde ĠLa ĠFr on tera . ĠThe Ġcorpus Ġcovers Ġthree Ġdialect s Ġof ĠMap ud ung un : Ġabout Ġ110 Ġhours Ġof ĠNg ul uc he , Ġ20 Ġhours Ġof ĠLaf ken che Ġand Ġ10 Ġhours Ġof ĠPew en che . ĠThe Ġthree Ġdialect s Ġare Ġquite Ġsimilar , Ġwith Ġsome Ġminor Ġsemantic Ġand Ġphon etic Ġdifferences . ĠThe Ġfourth Ġtraditionally Ġdistinguished Ġdialect , ĠHu ill iche , Ġhas Ġseveral Ġgram matical Ġdifferences Ġfrom Ġthe Ġother Ġthree Ġand Ġis Ġclassified Ġby ĠEth n ologue Ġas Ġa Ġseparate Ġlanguage , Ġiso Ġ6 39 - 3 : Ġhuh , Ġand Ġas Ġnearly Ġextinct . </s> The Ġrecordings Ġare Ġrestricted Ġto Ġa Ġsingle Ġdomain : Ġprimary , Ġpreventive , Ġand Ġtreatment Ġhealth Ġcare , Ġincluding Ġboth ĠWestern Ġand ĠMap uc he Ġtraditional Ġmedicine . ĠThe Ġrecording Ġsessions Ġwere Ġconducted Ġas Ġinteractive Ġconversations Ġso Ġas Ġto Ġbe Ġnatural Ġin ĠMap uc he Ġculture , Ġand Ġthey Ġwere Ġopen - ended , Ġfollowing Ġan Ġethn ographic Ġapproach . ĠThe Ġinterviewer Ġwas Ġtrained Ġin Ġthese Ġmethods Ġalong Ġwith Ġthe Ġuse Ġof Ġthe Ġdigital Ġrecording Ġsystems Ġthat Ġwere Ġavailable Ġat Ġthe Ġtime . ĠWe Ġalso Ġfollowed Ġhuman Ġsubject Ġprotocol . ĠEach Ġperson Ġsigned Ġa Ġconsent Ġform Ġto Ġrelease Ġthe Ġrecordings Ġfor Ġresearch Ġpurposes Ġand Ġthe Ġdata Ġhave Ġbeen Ġaccordingly Ġanonym ized . ĠBecause ĠMach i Ġ( traditional ĠMap uc he Ġheal ers ) Ġwere Ġinterviewed , Ġwe Ġasked Ġthe Ġtransc rib ers Ġto Ġdelete Ġany Ġculturally Ġproprietary Ġknowledge Ġthat Ġa ĠMach i Ġmay Ġhave Ġrevealed Ġduring Ġthe Ġconversation . ĠSimilarly , Ġwe Ġdeleted Ġany Ġnames Ġor Ġany Ġinformation Ġthat Ġmay Ġidentify Ġthe Ġparticipants . </s> The Ġcorpus Ġis Ġculturally Ġrelevant Ġbecause Ġit Ġwas Ġcreated Ġby ĠMap uc he Ġpeople , Ġusing Ġtraditional Ġways Ġof Ġrelating Ġto Ġeach Ġother Ġin Ġconversations . ĠThey Ġdiscussed Ġpersonal Ġexperiences Ġwith Ġprimary Ġhealth Ġcare Ġin Ġthe Ġtraditional ĠMap uc he Ġsystem Ġand Ġthe ĠChilean Ġhealth Ġcare Ġsystem , Ġtalking Ġabout Ġillnesses Ġand Ġthe Ġway Ġthey Ġwere Ġcured . ĠThe Ġparticipants Ġranged Ġfrom Ġ16 Ġyears Ġold Ġto Ġ100 Ġyears Ġold , Ġalmost Ġin Ġequal Ġnumbers Ġof Ġmen Ġand Ġwomen , Ġand Ġthey Ġwere Ġall Ġnative Ġspeakers Ġof ĠMap ud ung un . </s> The ĠResource Ġ: :: ĠOrth ography </s> At Ġthe Ġtime Ġof Ġthe Ġcollection Ġand Ġtranscription Ġof Ġthe Ġcorpus , Ġthe Ġorth ography Ġof ĠMap ud ung un Ġwas Ġnot Ġstandardized . ĠThe ĠMap uc he Ġteam Ġat Ġthe ĠInstit uto Ġde ĠEst ud ios ĠInd ÃŃ gen as Ġ( IE I ĠâĢĵ ĠInstitute Ġfor ĠIndigenous ĠStudies ) Ġdeveloped Ġa Ġsupra - dial ect al Ġalphabet Ġthat Ġcomprises Ġ28 Ġletters Ġthat Ġcover Ġ32 Ġphones Ġused Ġin Ġthe Ġthree ĠMap ud ung un Ġvariants . ĠThe Ġmain Ġcriterion Ġfor Ġchoosing Ġal ph abetic Ġcharacters Ġwas Ġto Ġuse Ġthe Ġcurrent ĠSpanish Ġkeyboard Ġthat Ġwas Ġavailable Ġon Ġall Ġcomputers Ġin ĠChilean Ġoffices Ġand Ġschools . ĠThe Ġalphabet Ġused Ġthe Ġsame Ġletters Ġused Ġin ĠSpanish Ġfor Ġthose Ġphon em es Ġthat Ġsound Ġlike ĠSpanish Ġphon em es . ĠDi ac rit ics Ġsuch Ġas Ġapost rop hes Ġwere Ġused Ġfor Ġsounds Ġthat Ġare Ġnot Ġfound Ġin ĠSpanish . </s> As Ġa Ġresult , Ġcertain Ġorth ographic Ġconventions Ġthat Ġwere Ġmade Ġat Ġthe Ġtime Ġdev iate Ġfrom Ġthe Ġnow - standard Ġorth ography Ġof ĠMap ud ung un , ĠAz um che fe . ĠWe Ġplan Ġto Ġnormal ize Ġthe Ġorth ography Ġof Ġthe Ġcorpus , Ġand Ġin Ġfact Ġa Ġsmall Ġsample Ġhas Ġalready Ġbeen Ġconverted Ġto Ġthe Ġmodern Ġorth ography . ĠHowever , Ġwe Ġbelieve Ġthat Ġthe Ġoriginal Ġtranscript ions Ġwill Ġalso Ġbe Ġinvaluable Ġfor Ġacademic , Ġhistorical , Ġand Ġcultural Ġpurposes , Ġhence Ġwe Ġrelease Ġthe Ġcorpus Ġusing Ġthese Ġconventions . </s> The ĠResource Ġ: :: ĠAdditional ĠAnnotations </s> In Ġaddition , Ġthe Ġtranscription Ġincludes Ġannotations Ġfor Ġnoises Ġand Ġdis flu encies Ġincluding Ġaborted Ġwords , Ġmis pron unci ations , Ġpoor Ġintellig ibility , Ġrepeated Ġand Ġcorrected Ġwords , Ġfalse Ġstarts , Ġhes itations , Ġundefined Ġsound Ġor Ġpron unci ations , Ġnon - verbal Ġartic ulations , Ġand Ġpauses . ĠForeign Ġwords , Ġin Ġthis Ġcase ĠSpanish Ġwords , Ġare Ġalso Ġlabelled Ġas Ġsuch . </s> The ĠResource Ġ: :: ĠClean ing </s> The Ġdialog ues Ġwere Ġoriginally Ġrecorded Ġusing Ġa ĠSony ĠD AT Ġrecorder Ġ( 48 kHz ), Ġmodel ĠT CD - D 8 , Ġand ĠSony Ġdigital Ġstereo Ġmicrophone , Ġmodel ĠEC M - DS 70 P . ĠTrans cription Ġwas Ġperformed Ġwith Ġthe ĠTrans Edit Ġtranscription Ġtool Ġv . 1 . 1 Ġbeta Ġ10 , Ġwhich Ġsynchron izes Ġthe Ġtransc ribed Ġtext Ġand Ġthe Ġwave Ġfiles . </s> However , Ġwe Ġfound Ġthat Ġa Ġnon - t riv ial Ġnumber Ġof Ġthe Ġutter ance Ġboundaries Ġand Ġspeaker Ġannotations Ġwere Ġflawed . ĠAlso Ġsome Ġrecording Ġsessions Ġdid Ġnot Ġhave Ġa Ġcomplete Ġset Ġof Ġmatching Ġaudio , Ġtranscription , Ġand Ġtranslation Ġfiles . ĠHence , Ġin Ġan Ġeffort Ġto Ġprovide Ġa Ġrelatively ĠâĢ ľ clean " Ġcorpus Ġfor Ġmodern Ġcomputational Ġexperiments , Ġwe Ġconverted Ġthe Ġencoding Ġof Ġthe Ġtextual Ġtranscription Ġfrom ĠLatin - 1 Ġto ĠUnicode , ĠDOS Ġto ĠUN IX Ġline Ġendings , Ġa Ġnow Ġmore Ġstandard Ġtext Ġencoding Ġformat Ġthan Ġwhat Ġwas Ġused Ġwhen Ġthe Ġdata Ġwas Ġfirst Ġcollected . ĠAdditionally , Ġwe Ġrenamed Ġa Ġsmall Ġportion Ġof Ġfiles Ġwhich Ġhad Ġbeen Ġmis named Ġand Ġremoved Ġseveral Ġduplicate Ġfiles . </s> Although Ġall Ġof Ġthe Ġdata Ġwas Ġrecorded Ġwith Ġsimilar Ġequipment Ġin Ġrelatively Ġquiet Ġenvironments , Ġthe Ġac oust ics Ġare Ġnot Ġas Ġuniform Ġas Ġwe Ġwould Ġlike Ġfor Ġbuilding Ġspeech Ġsynthes izers . ĠThus Ġwe Ġapplied Ġstandardized Ġpower Ġnormal ization . ĠWe Ġalso Ġmoved Ġthe Ġboundaries Ġof Ġthe Ġturns Ġto Ġstandard ize Ġthe Ġamount Ġof Ġleading Ġand Ġtrailing Ġsilence Ġin Ġeach Ġturn . ĠThis Ġis Ġa Ġstandard Ġprocedure Ġfor Ġspeech Ġrecognition Ġand Ġsynthesis Ġdatasets . ĠFinally Ġwe Ġused Ġthe Ġtechniques Ġin ĠB IB REF 7 Ġfor Ġfound Ġdata Ġto Ġre - align Ġthe Ġtext Ġto Ġthe Ġaudio Ġand Ġfind Ġout Ġwhich Ġturns Ġare Ġbest Ġ( or Ġworst ) Ġaligned Ġso Ġthat Ġwe Ġcan Ġselect Ġsegments Ġthat Ġgive Ġthe Ġmost Ġaccurate Ġalign ments . ĠSome Ġof Ġthe Ġmis align ments Ġmay Ġin Ġpart Ġbe Ġdue Ġto Ġvaried Ġorth ography , Ġand Ġwe Ġintend , Ġbut Ġhave Ġnot Ġyet , Ġto Ġinvestigate Ġnormal ization Ġof Ġorth ography Ġ( i . e . Ġspelling Ġcorrection ) Ġto Ġmitigate Ġthis . </s> The ĠResource Ġ: :: ĠTraining , ĠDev , Ġand ĠTest ĠSpl its </s> We Ġcreate Ġtwo Ġtraining Ġsets , Ġone Ġappropriate Ġfor Ġsingle - spe aker Ġspeech Ġsynthesis Ġexperiments , Ġand Ġone Ġappropriate Ġfor Ġmultiple - spe aker Ġspeech Ġrecognition Ġand Ġmachine Ġtranslation Ġexperiments . ĠIn Ġboth Ġcases , Ġour Ġtraining , Ġdevelopment , Ġand Ġtest Ġsplits Ġare Ġperformed Ġat Ġthe Ġdialogue Ġlevel , Ġso Ġthat Ġall Ġexamples Ġfrom Ġeach Ġdialogue Ġbelong Ġto Ġexactly Ġone Ġof Ġthese Ġsets . </s> For Ġsingle - spe aker Ġspeech Ġsynthesis , Ġwe Ġonly Ġuse Ġthe Ġdialog Ġturns Ġof Ġthe Ġspeaker Ġwith Ġthe Ġlargest Ġvolume Ġof Ġdata Ġ( n ml ch ĠâĢĵ Ġone Ġof Ġthe Ġinterview ers ). ĠThe Ġtraining Ġset Ġincludes Ġ$ 221 . 8 $ Ġthousand Ġsentences Ġfrom Ġ285 Ġdialog ues , Ġwith Ġ12 Ġand Ġ46 Ġconversations Ġreserved Ġfor Ġthe Ġdevelopment Ġand Ġtest Ġset . </s> For Ġspeech Ġrecognition Ġexperiments , Ġwe Ġensure Ġthat Ġour Ġtest Ġset Ġincludes Ġunique Ġspeakers Ġas Ġwell Ġas Ġspeakers Ġthat Ġoverlap Ġwith Ġthe Ġtraining Ġset , Ġin Ġorder Ġto Ġallow Ġfor Ġcomparisons Ġof Ġthe Ġability Ġof Ġthe Ġspeech Ġrecognition Ġsystem Ġto Ġgeneral ize Ġover Ġseen Ġand Ġnew Ġspeakers . ĠFor Ġconsistency , Ġwe Ġuse Ġthe Ġsame Ġdataset Ġsplits Ġfor Ġthe Ġmachine Ġtranslation Ġexperiments . ĠThe Ġstatistics Ġin ĠTable Ġreflect Ġthis Ġsplit . </s> Applications </s> Our Ġresource Ġhas Ġthe Ġpotential Ġto Ġbe Ġthe Ġbasis Ġof Ġcomputational Ġresearch Ġin ĠMap ud ung un Ġacross Ġseveral Ġareas . ĠSince Ġthe Ġcollected Ġaudio Ġhas Ġbeen Ġtransc ribed , Ġour Ġresource Ġis Ġappropriate Ġfor Ġthe Ġstudy Ġof Ġautomatic Ġspeech Ġrecognition Ġand Ġspeech Ġsynthesis . ĠThe ĠSpanish Ġtranslations Ġenable Ġthe Ġcreation Ġof Ġmachine Ġtranslation Ġsystems Ġbetween ĠMap ud ung un Ġand ĠSpanish , Ġas Ġwell Ġas Ġend - to - end Ġ( or Ġdirect ) Ġspeech Ġtranslation . ĠWe Ġin Ġfact Ġbuilt Ġsuch Ġspeech Ġsynthesis , Ġspeech Ġrecognition , Ġand Ġmachine Ġtranslation Ġsystems Ġas Ġa Ġshowcase Ġof Ġthe Ġusefulness Ġof Ġour Ġcorpus Ġin Ġthat Ġresearch Ġdirection . </s> Furthermore , Ġour Ġannotations Ġof Ġthe ĠSpanish Ġwords Ġinter sp ersed Ġin ĠMap ud ung un Ġspeech Ġcould Ġallow Ġfor Ġa Ġstudy Ġof Ġcode - sw itching Ġpatterns Ġwithin Ġthe ĠMap uc he Ġcommunity . ĠIn Ġaddition , Ġour Ġannotations Ġof Ġnon - standard ized Ġorth ographic Ġtranscript ions Ġcould Ġbe Ġextremely Ġuseful Ġin Ġthe Ġstudy Ġof Ġhistorical Ġlanguage Ġand Ġorth ography Ġchange Ġas Ġa Ġlanguage Ġmoves Ġfrom Ġpredominantly Ġoral Ġto Ġbeing Ġwritten Ġin Ġa Ġstandardized Ġorth ography , Ġas Ġwell Ġas Ġin Ġbuilding Ġspelling Ġnormal ization Ġand Ġcorrection Ġsystems . ĠThe Ġrelatively Ġlarge Ġamount Ġof Ġdata Ġthat Ġwe Ġcollected Ġwill Ġalso Ġallow Ġfor Ġthe Ġtraining Ġof Ġlarge Ġlanguage Ġmodels , Ġwhich Ġin Ġturn Ġcould Ġbe Ġused Ġas Ġthe Ġbasis Ġfor Ġpredictive Ġkeyboards Ġtailored Ġto ĠMap ud ung un . ĠLast , Ġsince Ġall Ġdata Ġare Ġdialog ues Ġannot ated Ġfor Ġthe Ġdifferent Ġspeaker Ġturns , Ġthey Ġcould Ġbe Ġuseful Ġfor Ġbuilding ĠMap ud ung un Ġdialogue Ġsystems Ġand Ġchat bot - like Ġapplications . </s> The Ġpotential Ġapplications Ġof Ġour Ġresource , Ġhowever , Ġare Ġnot Ġexhausted Ġin Ġlanguage Ġtechnologies . ĠThe Ġresource Ġas Ġa Ġwhole Ġcould Ġbe Ġinvaluable Ġfor Ġethn ographic Ġand Ġsoc iological Ġresearch , Ġas Ġthe Ġconversations Ġcontrast Ġtraditional Ġand ĠWestern Ġmedicine Ġpractices , Ġand Ġthey Ġcould Ġreveal Ġinteresting Ġaspects Ġof Ġthe ĠMap uc he Ġculture . </s> In Ġaddition , Ġthe Ġcorpus Ġis Ġa Ġgold mine Ġof Ġdata Ġfor Ġstudying Ġthe Ġmorph ost ynt ax Ġof ĠMap ud ung un ĠB IB REF 8 . ĠAs Ġan Ġisolate Ġpoly sy nt hetic Ġlanguage , Ġthe Ġstudy Ġof ĠMap ud ung un Ġcan Ġprovide Ġinsights Ġinto Ġthe Ġrange Ġof Ġpossibilities Ġwithin Ġhuman Ġlanguages Ġcan Ġwork . </s> Bas eline ĠResults </s> Using Ġthe Ġaforementioned Ġhigher Ġquality Ġportions Ġof Ġthe Ġcorpus , Ġwe Ġtrained Ġbaseline Ġsystems Ġfor ĠMap ud ung un Ġspeech Ġrecognition Ġand Ġspeech Ġsynthesis , Ġas Ġwell Ġas ĠMachine ĠTranslation Ġsystems Ġbetween ĠMap ud ung un Ġand ĠSpanish . </s> Bas eline ĠResults Ġ: :: ĠSpeech ĠSynt hesis </s> In Ġour Ġprevious Ġwork Ġon Ġbuilding Ġspeech Ġsystems Ġon Ġfound Ġdata Ġin Ġ700 Ġlanguages , ĠB IB REF 7 , Ġwe Ġaddressed Ġalignment Ġissues Ġ( when Ġaudio Ġis Ġnot Ġsegment ed Ġinto Ġturn / sent ence Ġsized Ġchunks ) Ġand Ġcorrectness Ġissues Ġ( when Ġthe Ġaudio Ġdoes Ġnot Ġmatch Ġthe Ġtranscription ). ĠWe Ġused Ġthe Ġsame Ġtechniques Ġhere , Ġas Ġdescribed Ġabove . </s> For Ġthe Ġbest Ġquality Ġspeech Ġsynthesis Ġwe Ġneed Ġa Ġfew Ġhours Ġof Ġphon etically - balanced , Ġsingle - spe aker , Ġread Ġspeech . ĠOur Ġfirst Ġstep Ġwas Ġto Ġuse Ġthe Ġstart Ġand Ġend Ġpoints Ġfor Ġeach Ġturn Ġin Ġthe Ġdialog ues , Ġand Ġselect Ġthose Ġof Ġthe Ġmost Ġfrequent Ġspeaker , Ġn ml ch . ĠThis Ġgave Ġus Ġaround Ġ18 250 Ġsegments . ĠWe Ġfurther Ġautomatically Ġremoved Ġexcessive Ġsilence Ġfrom Ġthe Ġstart , Ġmiddle Ġand Ġend Ġof Ġthese Ġturns Ġ( based Ġon Ġoccurrence Ġof ĠF 0 ). ĠThis Ġgave Ġus Ġ13 Ġhours Ġand Ġ48 Ġminutes Ġof Ġspeech . </s> We Ġphon etically Ġaligned Ġthis Ġdata Ġand Ġbuilt Ġa Ġspeech Ġcluster gen Ġstatistical Ġspeech Ġsynthes izer ĠB IB REF 9 Ġfrom Ġall Ġof Ġthis Ġdata . ĠWe Ġres ynt hes ized Ġall Ġof Ġthe Ġdata Ġand Ġmeasured Ġthe Ġdifference Ġbetween Ġthe Ġsynthes ized Ġdata Ġand Ġthe Ġoriginal Ġdata Ġusing ĠMel ĠC ep st ral ĠDist ortion , Ġa Ġstandard Ġmethod Ġfor Ġautomatically Ġmeasuring Ġquality Ġof Ġspeech Ġgeneration ĠB IB REF 10 . ĠWe Ġthen Ġordered Ġthe Ġsegments Ġby Ġtheir Ġgeneration Ġscore Ġand Ġtook Ġthe Ġtop Ġ2000 Ġturns Ġto Ġbuild Ġa Ġnew Ġsynthes izer , Ġassuming Ġthe Ġbetter Ġscores Ġcorrespond ed Ġto Ġbetter Ġalign ments , Ġfollowing Ġthe Ġtechniques Ġof ĠB IB REF 7 . </s> The Ġinitial Ġbuild Ġgave Ġan ĠM CD Ġon Ġheld Ġout Ġdata Ġof Ġ6 . 483 . ĠWhile Ġthe Ġ2000 Ġbest Ġsegment Ġdataset Ġgives Ġan ĠM CD Ġof Ġ5 . 551 , Ġwhich Ġis Ġa Ġlarge Ġimprovement . ĠThe Ġquality Ġof Ġthe Ġgenerated Ġspeech Ġgoes Ġfrom Ġunderstandable , Ġonly Ġif Ġyou Ġcan Ġsee Ġthe Ġtext , Ġto Ġunderstandable , Ġand Ġtransc rib able Ġeven Ġfor Ġnon - Map ud ung un Ġspeakers . ĠWe Ġdo Ġnot Ġbelieve Ġwe Ġare Ġbuilding Ġthe Ġbest Ġsynthes izer Ġwith Ġour Ġcurrent Ġ( non - ne ural ) Ġtechniques , Ġbut Ġwe Ġdo Ġbelieve Ġwe Ġare Ġselecting Ġthe Ġbest Ġtraining Ġdata Ġfor Ġother Ġstatistical Ġand Ġneural Ġtraining Ġtechniques Ġin Ġboth Ġspeech Ġsynthesis Ġand Ġspeech Ġrecognition . </s> Bas eline ĠResults Ġ: :: ĠSpeech ĠRecogn ition </s> For Ġspeech Ġrecognition Ġ( AS R ) Ġwe Ġused ĠK aldi ĠB IB REF 11 . ĠAs Ġwe Ġdo Ġnot Ġhave Ġaccess Ġto Ġpronunciation Ġlex ica Ġfor ĠMap ud ung un , Ġwe Ġhad Ġto Ġapproximate Ġthem Ġwith Ġtwo Ġsettings . ĠIn Ġthe Ġfirst Ġsetting , Ġwe Ġmake Ġthe Ġsimple Ġassumption Ġthat Ġeach Ġcharacter Ġcorresponds Ġto Ġa Ġpron un ced Ġphon eme . ĠIn Ġthe Ġsecond Ġsetting , Ġwe Ġinstead Ġused Ġthe Ġgenerated Ġphon etic Ġlex icon Ġalso Ġused Ġin Ġthe Ġabove - mentioned Ġspeech Ġsynthesis Ġtechniques . ĠThe Ġtrain / dev / test Ġsplits Ġare Ġacross Ġconversations , Ġas Ġdescribed Ġabove . </s> Under Ġthe Ġfirst Ġsetting , Ġwe Ġobtained Ġa Ġ60 % Ġcharacter Ġerror Ġrate , Ġwhile Ġthe Ġgenerated Ġlex icon Ġsignificantly Ġboosts Ġperformance , Ġas Ġour Ġsystems Ġachieve Ġa Ġnotably Ġreduced Ġ30 % Ġphone Ġerror Ġrate . ĠNaturally , Ġthese Ġresults Ġare Ġrelatively Ġfar Ġfrom Ġthe Ġquality Ġof ĠAS R Ġsystems Ġtrained Ġon Ġlarge Ġamounts Ġof Ġclean Ġdata Ġsuch Ġas Ġthose Ġavailable Ġin ĠEnglish . ĠGiven Ġthe Ġquality Ġof Ġthe Ġrecordings , Ġand Ġthe Ġlack Ġof Ġadditional Ġresources , Ġwe Ġconsider Ġour Ġresults Ġfairly Ġreasonable Ġand Ġthey Ġwould Ġstill Ġbe Ġusable Ġfor Ġsimple Ġdialog - like Ġtasks . ĠWe Ġanticipate , Ġthough , Ġthat Ġone Ġcould Ġsignificantly Ġimprove ĠAS R Ġquality Ġover Ġour Ġdataset , Ġby Ġusing Ġin - domain Ġlanguage Ġmodels , Ġor Ġby Ġtraining Ġend - to - end Ġneural Ġrecogn izers Ġleveraging Ġlanguages Ġwith Ġsimilar Ġphon etic Ġinvent ories ĠB IB REF 12 Ġor Ġby Ġusing Ġthe Ġavailable ĠSpanish Ġtranslations Ġin Ġa Ġmulti - source Ġscenario ĠB IB REF 13 . </s> Bas eline ĠResults Ġ: :: ĠMap ud ung un âĢĵ Spanish ĠMachine ĠTranslation </s> We Ġbuilt Ġneural Ġend - to - end Ġmachine Ġtranslation Ġsystems Ġbetween ĠMap ud ung un Ġand ĠSpanish Ġin Ġboth Ġdirections , Ġusing Ġstate - of - the - art ĠTrans former Ġarchitecture ĠB IB REF 14 Ġwith Ġthe Ġtool kit Ġof ĠB IB REF 15 . ĠWe Ġtrain Ġour Ġsystems Ġat Ġthe Ġsub word Ġlevel Ġusing ĠByte - P air ĠEnc oding ĠB IB REF 16 Ġwith Ġa Ġvocabulary Ġof Ġ5000 Ġsub words , Ġshared Ġbetween Ġthe Ġsource Ġand Ġtarget Ġlanguages . ĠWe Ġuse Ġfive Ġlayers Ġfor Ġeach Ġof Ġthe Ġenc oder Ġand Ġthe Ġdec oder , Ġan Ġembed ding Ġsize Ġof Ġ512 , Ġfeed Ġforward Ġtransformation Ġsize Ġof Ġ2048 , Ġand Ġeight Ġattention Ġheads . ĠWe Ġuse Ġdrop out ĠB IB REF 17 Ġwith Ġ$ 0 . 4 $ Ġprobability Ġas Ġwell Ġas Ġlabel Ġsmoot hing Ġset Ġto Ġ$ 0 . 1 $. ĠWe Ġtrain Ġwith Ġthe ĠAdam Ġoptim izer ĠB IB REF 18 Ġfor Ġup Ġto Ġ200 Ġepoch s Ġusing Ġlearning Ġdecay Ġwith Ġa Ġpatience Ġof Ġsix Ġepoch s . </s> The Ġbaseline Ġresults Ġusing Ġdifferent Ġportions Ġof Ġthe Ġtraining Ġset Ġ( 10 k , Ġ50 k , Ġ100 k , Ġand Ġall Ġ( 220 k ) Ġparallel Ġsentences ) Ġon Ġboth Ġtranslation Ġdirections Ġare Ġpresented Ġin ĠTable Ġ, Ġusing Ġdet oken ized ĠB LE U ĠB IB REF 19 Ġ( a Ġstandard ĠMT Ġmetric ) Ġand Ġch r F ĠB IB REF 20 Ġ( a Ġmetric Ġthat Ġwe Ġconsider Ġto Ġbe Ġmore Ġappropriate Ġfor Ġpoly sy nt hetic Ġlanguages , Ġas Ġit Ġdoes Ġnot Ġrely Ġon Ġword Ġn - gram s ) Ġcomputed Ġwith Ġthe Ġsac re BLE U Ġtool kit ĠB IB REF 21 . ĠIt Ġit Ġworth Ġnoting Ġthe Ġdifference Ġin Ġquality Ġbetween Ġthe Ġtwo Ġdirections , Ġwith Ġtranslation Ġinto ĠSpanish Ġreaching Ġ20 . 4 Ġ( almost Ġ21 ) ĠB LE U Ġpoints Ġin Ġthe Ġdevelopment Ġset , Ġwhile Ġthe Ġopposite Ġdirection Ġ( trans l ating Ġinto ĠMap ud ung un ) Ġshows Ġabout Ġa Ġ7 ĠB LE U Ġpoints Ġworse Ġperformance . ĠThis Ġis Ġmost Ġlikely Ġdue Ġto ĠMap ud ung un Ġbeing Ġa Ġpoly sy nt hetic Ġlanguage , Ġwith Ġits Ġcomplicated Ġmorphology Ġposing Ġa Ġchallenge Ġfor Ġproper Ġgeneration . </s> Related ĠWork </s> Map ud ung un Ġgrammar Ġhas Ġbeen Ġstudied Ġsince Ġthe Ġarrival Ġof ĠEuropean Ġmissionaries Ġand Ġcolon izers Ġhundreds Ġof Ġyears Ġago . ĠMore Ġrecent Ġdescriptions Ġof ĠMap ud ung un Ġgrammar ĠB IB REF 1 Ġand ĠB IB REF 0 Ġinformed Ġthe Ġcollection Ġof Ġthe Ġresource Ġthat Ġwe Ġare Ġpresenting Ġin Ġthis Ġpaper . </s> Port ions Ġof Ġour Ġresource Ġhave Ġbeen Ġused Ġin Ġearly Ġefforts Ġto Ġbuild Ġlanguage Ġsystems Ġfor ĠMap ud ung un . ĠIn Ġparticular , ĠB IB REF 22 Ġfocused Ġon ĠMap ud ung un Ġmorphology Ġin Ġorder Ġto Ġcreate Ġspelling Ġcorrection Ġsystems , Ġwhile ĠB IB REF 23 , ĠB IB REF 6 , ĠB IB REF 24 , Ġand ĠB IB REF 25 Ġdeveloped Ġhybrid Ġrule - Ġand Ġphrase - based ĠStatistical ĠMachine ĠTranslation Ġsystems . </s> Naturally , Ġsimilar Ġworks Ġin Ġcollecting Ġcorpor a Ġin ĠIndigenous Ġlanguages Ġof ĠLatin ĠAmerica Ġare Ġabundant , Ġbut Ġvery Ġfew , Ġif Ġany , Ġhave Ġthe Ġscale Ġand Ġpotential Ġof Ġour Ġresource Ġto Ġbe Ġuseful Ġin Ġmany Ġdownstream Ġlanguage - specific Ġand Ġinter - disciplinary Ġapplications . ĠA Ġgeneral Ġoverview Ġof Ġthe Ġstate Ġof ĠN LP Ġfor Ġthe Ġunder - represented Ġlanguages Ġof Ġthe ĠAmericas Ġcan Ġbe Ġfound Ġat ĠB IB REF 26 . ĠTo Ġname Ġa Ġfew Ġof Ġthe Ġmany Ġnotable Ġworks , ĠB IB REF 27 Ġcreated Ġa Ġparallel ĠMix tec - Spanish Ġcorpus Ġfor ĠMachine ĠTranslation Ġand ĠB IB REF 28 Ġcreated Ġlex ical Ġresources Ġfor ĠA rap aho , Ġwhile ĠB IB REF 29 Ġand ĠB IB REF 30 Ġfocused Ġon Ġbuilding Ġspeech Ġcorpor a Ġfor ĠSouthern ĠQue ch ua Ġand ĠChat ino Ġrespectively . </s> Conclusion </s> With Ġthis Ġwork Ġwe Ġpresent Ġa Ġresource Ġthat Ġwill Ġbe Ġextremely Ġuseful Ġfor Ġbuilding Ġlanguage Ġsystems Ġin Ġan Ġendangered , Ġunder - represented Ġlanguage , ĠMap ud ung un . ĠWe Ġbenchmark ĠN LP Ġsystems Ġfor Ġspeech Ġsynthesis , Ġspeech Ġrecognition , Ġand Ġmachine Ġtranslation , Ġproviding Ġstrong Ġbaseline Ġresults . ĠThe Ġsize Ġof Ġour Ġresource Ġ( 142 Ġhours , Ġmore Ġthan Ġ260 k Ġtotal Ġsentences ) Ġhas Ġthe Ġpotential Ġto Ġalleviate Ġmany Ġof Ġthe Ġissues Ġfaced Ġwhen Ġbuilding Ġlanguage Ġtechnologies Ġfor ĠMap ud ung un , Ġin Ġcontrast Ġto Ġother Ġindigenous Ġlanguages Ġof Ġthe ĠAmericas Ġthat Ġunfortunately Ġremain Ġlow - resource . </s> Our Ġresource Ġcould Ġalso Ġbe Ġused Ġfor Ġethn ographic Ġand Ġanthrop ological Ġresearch Ġinto Ġthe ĠMap uc he Ġculture , Ġand Ġhas Ġthe Ġpotential Ġto Ġcontribute Ġto Ġinter cultural Ġbilingual Ġeducation , Ġpreservation Ġactivities Ġand Ġfurther Ġgeneral Ġadvancement Ġof Ġthe ĠMap ud ung un - speaking Ġcommunity . </s> Acknowled gements </s> The Ġdata Ġcollection Ġdescribed Ġin Ġthis Ġpaper Ġwas Ġsupported Ġby ĠNS F Ġgrants ĠI IS - 01 216 31 Ġ( AV EN UE ) Ġand ĠI IS - 05 34 217 Ġ( LE TR AS ), Ġwith Ġsupplemental Ġfunding Ġfrom ĠNS F 's ĠOffice Ġof ĠInternational ĠScience Ġand ĠEducation . ĠPrel iminary Ġfunding Ġfor Ġwork Ġon ĠMap ud ung un Ġwas Ġalso Ġprovided Ġby ĠDAR PA ĠThe Ġexperimental Ġmaterial Ġis Ġbased Ġupon Ġwork Ġgenerously Ġsupported Ġby Ġthe ĠNational ĠScience ĠFoundation Ġunder Ġgrant Ġ176 15 48 .
Introduction Recent years have seen unprecedented progress for Natural Language Processing (NLP) on almost every NLP subtask. Even though low-resource settings have also been explored, this progress has overwhelmingly been observed in languages with significant data resources that can be leveraged to train deep neural networks. Low-resource languages still lag behind. Endangered languages pose an additional challenge. The process of documenting an endangered language typically includes the creation of word lists, audio and video recordings, notes, or grammar fragments, with the created resources then stored in large online linguistics archives. This process is often hindered by the Transcription Bottleneck: the linguistic fieldworker and the language community may not have time to transcribe all of the recordings and may only transcribe segments that are linguistically salient for publication or culturally significant for the creation of community resources. With this work we make publicly available a large corpus in Mapudungun, a language of the indigenous Mapuche people of southern Chile and western Argentina. We hope to ameliorate the resource gap and the transcription bottleneck in two ways. First, we are providing a larger data set than has previously been available, and second, we are providing baselines for NLP tasks (speech recognition, speech synthesis, and machine translation). In providing baselines and datasets splits, we hope to further facilitate research on low-resource NLP for this language through our data set. Research on low-resource speech recognition is particularly important in relieving the transcription bottleneck, while tackling the research challenges that speech synthesis and machine translation pose for such languages could lead to such systems being deployed to serve more under-represented communities. The Mapudungun Language Mapudungun (iso 639-3: arn) is an indigenous language of the Americas spoken natively in Chile and Argentina, with an estimated 100 to 200 thousand speakers in Chile and 27 to 60 thousand speakers in Argentina BIBREF0. It is an isolate language and is classified as threatened by Ethnologue, hence the critical importance of all documentary efforts. Although the morphology of nouns is relatively simple, Mapudungun verb morphology is highly agglutinative and complex. Some analyses provide as many as 36 verb suffix slots BIBREF1. A typical complex verb form occurring in our corpus of spoken Mapudungun consists of five or six morphemes. Mapudungun has several interesting grammatical properties. It is a polysynthetic language in the sense of BIBREF2; see BIBREF3 for explicit argumentation. As with other polysynthetic languages, Mapudungun has Noun Incorporation; however, it is unique insofar as the Noun appears to the right of the Verb, instead of to the left, as in most polysynthetic languages BIBREF4. One further distinction of Mapudungun is that, whereas other polysynthetic languages are characterized by a lack of infinitives, Mapudungun has infinitival verb forms; that is, while subordinate clauses in Mapudungun closely resemble possessed nominals and may occur with an analytic marker resembling possessor agreement, there is no agreement inflection on the verb itself. One further remarkable property of Mapudungun is its inverse voice system of agreement, whereby the highest agreement is with the argument highest in an animacy hierarchy regardless of thematic role BIBREF5. The Resource The resource is comprised of 142 hours of spoken Mapudungun that was recorded during the AVENUE project BIBREF6 in 2001 to 2005. The data was recorded under a partnership between the AVENUE project, funded by the US National Science Foundation at Carnegie Mellon University, the Chilean Ministry of Education (Mineduc), and the Instituto de Estudios Indígenas at Universidad de La Frontera, originally spanning 170 hours of audio. We have recently cleaned the data and are releasing it publicly for the first time (although it has been shared with individual researchers in the past) along with NLP baselines. The recordings were transcribed and translated into Spanish at the Instituto de Estudios Indígenas at Universidad de La Frontera. The corpus covers three dialects of Mapudungun: about 110 hours of Nguluche, 20 hours of Lafkenche and 10 hours of Pewenche. The three dialects are quite similar, with some minor semantic and phonetic differences. The fourth traditionally distinguished dialect, Huilliche, has several grammatical differences from the other three and is classified by Ethnologue as a separate language, iso 639-3: huh, and as nearly extinct. The recordings are restricted to a single domain: primary, preventive, and treatment health care, including both Western and Mapuche traditional medicine. The recording sessions were conducted as interactive conversations so as to be natural in Mapuche culture, and they were open-ended, following an ethnographic approach. The interviewer was trained in these methods along with the use of the digital recording systems that were available at the time. We also followed human subject protocol. Each person signed a consent form to release the recordings for research purposes and the data have been accordingly anonymized. Because Machi (traditional Mapuche healers) were interviewed, we asked the transcribers to delete any culturally proprietary knowledge that a Machi may have revealed during the conversation. Similarly, we deleted any names or any information that may identify the participants. The corpus is culturally relevant because it was created by Mapuche people, using traditional ways of relating to each other in conversations. They discussed personal experiences with primary health care in the traditional Mapuche system and the Chilean health care system, talking about illnesses and the way they were cured. The participants ranged from 16 years old to 100 years old, almost in equal numbers of men and women, and they were all native speakers of Mapudungun. The Resource ::: Orthography At the time of the collection and transcription of the corpus, the orthography of Mapudungun was not standardized. The Mapuche team at the Instituto de Estudios Indígenas (IEI – Institute for Indigenous Studies) developed a supra-dialectal alphabet that comprises 28 letters that cover 32 phones used in the three Mapudungun variants. The main criterion for choosing alphabetic characters was to use the current Spanish keyboard that was available on all computers in Chilean offices and schools. The alphabet used the same letters used in Spanish for those phonemes that sound like Spanish phonemes. Diacritics such as apostrophes were used for sounds that are not found in Spanish. As a result, certain orthographic conventions that were made at the time deviate from the now-standard orthography of Mapudungun, Azumchefe. We plan to normalize the orthography of the corpus, and in fact a small sample has already been converted to the modern orthography. However, we believe that the original transcriptions will also be invaluable for academic, historical, and cultural purposes, hence we release the corpus using these conventions. The Resource ::: Additional Annotations In addition, the transcription includes annotations for noises and disfluencies including aborted words, mispronunciations, poor intelligibility, repeated and corrected words, false starts, hesitations, undefined sound or pronunciations, non-verbal articulations, and pauses. Foreign words, in this case Spanish words, are also labelled as such. The Resource ::: Cleaning The dialogues were originally recorded using a Sony DAT recorder (48kHz), model TCD-D8, and Sony digital stereo microphone, model ECM-DS70P. Transcription was performed with the TransEdit transcription tool v.1.1 beta 10, which synchronizes the transcribed text and the wave files. However, we found that a non-trivial number of the utterance boundaries and speaker annotations were flawed. Also some recording sessions did not have a complete set of matching audio, transcription, and translation files. Hence, in an effort to provide a relatively “clean" corpus for modern computational experiments, we converted the encoding of the textual transcription from Latin-1 to Unicode, DOS to UNIX line endings, a now more standard text encoding format than what was used when the data was first collected. Additionally, we renamed a small portion of files which had been misnamed and removed several duplicate files. Although all of the data was recorded with similar equipment in relatively quiet environments, the acoustics are not as uniform as we would like for building speech synthesizers. Thus we applied standardized power normalization. We also moved the boundaries of the turns to standardize the amount of leading and trailing silence in each turn. This is a standard procedure for speech recognition and synthesis datasets. Finally we used the techniques in BIBREF7 for found data to re-align the text to the audio and find out which turns are best (or worst) aligned so that we can select segments that give the most accurate alignments. Some of the misalignments may in part be due to varied orthography, and we intend, but have not yet, to investigate normalization of orthography (i.e. spelling correction) to mitigate this. The Resource ::: Training, Dev, and Test Splits We create two training sets, one appropriate for single-speaker speech synthesis experiments, and one appropriate for multiple-speaker speech recognition and machine translation experiments. In both cases, our training, development, and test splits are performed at the dialogue level, so that all examples from each dialogue belong to exactly one of these sets. For single-speaker speech synthesis, we only use the dialog turns of the speaker with the largest volume of data (nmlch – one of the interviewers). The training set includes $221.8$ thousand sentences from 285 dialogues, with 12 and 46 conversations reserved for the development and test set. For speech recognition experiments, we ensure that our test set includes unique speakers as well as speakers that overlap with the training set, in order to allow for comparisons of the ability of the speech recognition system to generalize over seen and new speakers. For consistency, we use the same dataset splits for the machine translation experiments. The statistics in Table reflect this split. Applications Our resource has the potential to be the basis of computational research in Mapudungun across several areas. Since the collected audio has been transcribed, our resource is appropriate for the study of automatic speech recognition and speech synthesis. The Spanish translations enable the creation of machine translation systems between Mapudungun and Spanish, as well as end-to-end (or direct) speech translation. We in fact built such speech synthesis, speech recognition, and machine translation systems as a showcase of the usefulness of our corpus in that research direction. Furthermore, our annotations of the Spanish words interspersed in Mapudungun speech could allow for a study of code-switching patterns within the Mapuche community. In addition, our annotations of non-standardized orthographic transcriptions could be extremely useful in the study of historical language and orthography change as a language moves from predominantly oral to being written in a standardized orthography, as well as in building spelling normalization and correction systems. The relatively large amount of data that we collected will also allow for the training of large language models, which in turn could be used as the basis for predictive keyboards tailored to Mapudungun. Last, since all data are dialogues annotated for the different speaker turns, they could be useful for building Mapudungun dialogue systems and chatbot-like applications. The potential applications of our resource, however, are not exhausted in language technologies. The resource as a whole could be invaluable for ethnographic and sociological research, as the conversations contrast traditional and Western medicine practices, and they could reveal interesting aspects of the Mapuche culture. In addition, the corpus is a goldmine of data for studying the morphostyntax of Mapudungun BIBREF8. As an isolate polysynthetic language, the study of Mapudungun can provide insights into the range of possibilities within human languages can work. Baseline Results Using the aforementioned higher quality portions of the corpus, we trained baseline systems for Mapudungun speech recognition and speech synthesis, as well as Machine Translation systems between Mapudungun and Spanish. Baseline Results ::: Speech Synthesis In our previous work on building speech systems on found data in 700 languages, BIBREF7, we addressed alignment issues (when audio is not segmented into turn/sentence sized chunks) and correctness issues (when the audio does not match the transcription). We used the same techniques here, as described above. For the best quality speech synthesis we need a few hours of phonetically-balanced, single-speaker, read speech. Our first step was to use the start and end points for each turn in the dialogues, and select those of the most frequent speaker, nmlch. This gave us around 18250 segments. We further automatically removed excessive silence from the start, middle and end of these turns (based on occurrence of F0). This gave us 13 hours and 48 minutes of speech. We phonetically aligned this data and built a speech clustergen statistical speech synthesizer BIBREF9 from all of this data. We resynthesized all of the data and measured the difference between the synthesized data and the original data using Mel Cepstral Distortion, a standard method for automatically measuring quality of speech generation BIBREF10. We then ordered the segments by their generation score and took the top 2000 turns to build a new synthesizer, assuming the better scores corresponded to better alignments, following the techniques of BIBREF7. The initial build gave an MCD on held out data of 6.483. While the 2000 best segment dataset gives an MCD of 5.551, which is a large improvement. The quality of the generated speech goes from understandable, only if you can see the text, to understandable, and transcribable even for non-Mapudungun speakers. We do not believe we are building the best synthesizer with our current (non-neural) techniques, but we do believe we are selecting the best training data for other statistical and neural training techniques in both speech synthesis and speech recognition. Baseline Results ::: Speech Recognition For speech recognition (ASR) we used Kaldi BIBREF11. As we do not have access to pronunciation lexica for Mapudungun, we had to approximate them with two settings. In the first setting, we make the simple assumption that each character corresponds to a pronunced phoneme. In the second setting, we instead used the generated phonetic lexicon also used in the above-mentioned speech synthesis techniques. The train/dev/test splits are across conversations, as described above. Under the first setting, we obtained a 60% character error rate, while the generated lexicon significantly boosts performance, as our systems achieve a notably reduced 30% phone error rate. Naturally, these results are relatively far from the quality of ASR systems trained on large amounts of clean data such as those available in English. Given the quality of the recordings, and the lack of additional resources, we consider our results fairly reasonable and they would still be usable for simple dialog-like tasks. We anticipate, though, that one could significantly improve ASR quality over our dataset, by using in-domain language models, or by training end-to-end neural recognizers leveraging languages with similar phonetic inventories BIBREF12 or by using the available Spanish translations in a multi-source scenario BIBREF13. Baseline Results ::: Mapudungun–Spanish Machine Translation We built neural end-to-end machine translation systems between Mapudungun and Spanish in both directions, using state-of-the-art Transformer architecture BIBREF14 with the toolkit of BIBREF15. We train our systems at the subword level using Byte-Pair Encoding BIBREF16 with a vocabulary of 5000 subwords, shared between the source and target languages. We use five layers for each of the encoder and the decoder, an embedding size of 512, feed forward transformation size of 2048, and eight attention heads. We use dropout BIBREF17 with $0.4$ probability as well as label smoothing set to $0.1$. We train with the Adam optimizer BIBREF18 for up to 200 epochs using learning decay with a patience of six epochs. The baseline results using different portions of the training set (10k, 50k, 100k, and all (220k) parallel sentences) on both translation directions are presented in Table , using detokenized BLEU BIBREF19 (a standard MT metric) and chrF BIBREF20 (a metric that we consider to be more appropriate for polysynthetic languages, as it does not rely on word n-grams) computed with the sacreBLEU toolkit BIBREF21. It it worth noting the difference in quality between the two directions, with translation into Spanish reaching 20.4 (almost 21) BLEU points in the development set, while the opposite direction (translating into Mapudungun) shows about a 7 BLEU points worse performance. This is most likely due to Mapudungun being a polysynthetic language, with its complicated morphology posing a challenge for proper generation. Related Work Mapudungun grammar has been studied since the arrival of European missionaries and colonizers hundreds of years ago. More recent descriptions of Mapudungun grammar BIBREF1 and BIBREF0 informed the collection of the resource that we are presenting in this paper. Portions of our resource have been used in early efforts to build language systems for Mapudungun. In particular, BIBREF22 focused on Mapudungun morphology in order to create spelling correction systems, while BIBREF23, BIBREF6, BIBREF24, and BIBREF25 developed hybrid rule- and phrase-based Statistical Machine Translation systems. Naturally, similar works in collecting corpora in Indigenous languages of Latin America are abundant, but very few, if any, have the scale and potential of our resource to be useful in many downstream language-specific and inter-disciplinary applications. A general overview of the state of NLP for the under-represented languages of the Americas can be found at BIBREF26. To name a few of the many notable works, BIBREF27 created a parallel Mixtec-Spanish corpus for Machine Translation and BIBREF28 created lexical resources for Arapaho, while BIBREF29 and BIBREF30 focused on building speech corpora for Southern Quechua and Chatino respectively. Conclusion With this work we present a resource that will be extremely useful for building language systems in an endangered, under-represented language, Mapudungun. We benchmark NLP systems for speech synthesis, speech recognition, and machine translation, providing strong baseline results. The size of our resource (142 hours, more than 260k total sentences) has the potential to alleviate many of the issues faced when building language technologies for Mapudungun, in contrast to other indigenous languages of the Americas that unfortunately remain low-resource. Our resource could also be used for ethnographic and anthropological research into the Mapuche culture, and has the potential to contribute to intercultural bilingual education, preservation activities and further general advancement of the Mapudungun-speaking community. Acknowledgements The data collection described in this paper was supported by NSF grants IIS-0121631 (AVENUE) and IIS-0534217 (LETRAS), with supplemental funding from NSF's Office of International Science and Education. Preliminary funding for work on Mapudungun was also provided by DARPA The experimental material is based upon work generously supported by the National Science Foundation under grant 1761548.
What are the models used for the baseline of the three NLP tasks?
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What Ġare Ġthe Ġmodels Ġused Ġfor Ġthe Ġbaseline Ġof Ġthe Ġthree ĠN LP Ġtasks ?
state - of - the - art ĠTrans former Ġarchitecture , ĠK aldi , Ġspeech Ġcluster gen Ġstatistical Ġspeech Ġsynthes izer
<s> What Ġare Ġthe Ġmodels Ġused Ġfor Ġthe Ġbaseline Ġof Ġthe Ġthree ĠN LP Ġtasks ? </s> Introduction </s> Recent Ġyears Ġhave Ġseen Ġunprecedented Ġprogress Ġfor ĠNatural ĠLanguage ĠProcessing Ġ( N LP ) Ġon Ġalmost Ġevery ĠN LP Ġsubt ask . ĠEven Ġthough Ġlow - resource Ġsettings Ġhave Ġalso Ġbeen Ġexplored , Ġthis Ġprogress Ġhas Ġoverwhelmingly Ġbeen Ġobserved Ġin Ġlanguages Ġwith Ġsignificant Ġdata Ġresources Ġthat Ġcan Ġbe Ġlever aged Ġto Ġtrain Ġdeep Ġneural Ġnetworks . ĠLow - resource Ġlanguages Ġstill Ġlag Ġbehind . </s> End angered Ġlanguages Ġpose Ġan Ġadditional Ġchallenge . ĠThe Ġprocess Ġof Ġdocumenting Ġan Ġendangered Ġlanguage Ġtypically Ġincludes Ġthe Ġcreation Ġof Ġword Ġlists , Ġaudio Ġand Ġvideo Ġrecordings , Ġnotes , Ġor Ġgrammar Ġfragments , Ġwith Ġthe Ġcreated Ġresources Ġthen Ġstored Ġin Ġlarge Ġonline Ġlingu istics Ġarchives . ĠThis Ġprocess Ġis Ġoften Ġhindered Ġby Ġthe ĠTrans cription ĠBott leneck : Ġthe Ġlinguistic Ġfield worker Ġand Ġthe Ġlanguage Ġcommunity Ġmay Ġnot Ġhave Ġtime Ġto Ġtransc ribe Ġall Ġof Ġthe Ġrecordings Ġand Ġmay Ġonly Ġtransc ribe Ġsegments Ġthat Ġare Ġlingu istically Ġsalient Ġfor Ġpublication Ġor Ġculturally Ġsignificant Ġfor Ġthe Ġcreation Ġof Ġcommunity Ġresources . </s> With Ġthis Ġwork Ġwe Ġmake Ġpublicly Ġavailable Ġa Ġlarge Ġcorpus Ġin ĠMap ud ung un , Ġa Ġlanguage Ġof Ġthe Ġindigenous ĠMap uc he Ġpeople Ġof Ġsouthern ĠChile Ġand Ġwestern ĠArgentina . ĠWe Ġhope Ġto Ġam el ior ate Ġthe Ġresource Ġgap Ġand Ġthe Ġtranscription Ġbottleneck Ġin Ġtwo Ġways . ĠFirst , Ġwe Ġare Ġproviding Ġa Ġlarger Ġdata Ġset Ġthan Ġhas Ġpreviously Ġbeen Ġavailable , Ġand Ġsecond , Ġwe Ġare Ġproviding Ġbas elines Ġfor ĠN LP Ġtasks Ġ( speech Ġrecognition , Ġspeech Ġsynthesis , Ġand Ġmachine Ġtranslation ). ĠIn Ġproviding Ġbas elines Ġand Ġdatasets Ġsplits , Ġwe Ġhope Ġto Ġfurther Ġfacilitate Ġresearch Ġon Ġlow - resource ĠN LP Ġfor Ġthis Ġlanguage Ġthrough Ġour Ġdata Ġset . ĠResearch Ġon Ġlow - resource Ġspeech Ġrecognition Ġis Ġparticularly Ġimportant Ġin Ġrelie ving Ġthe Ġtranscription Ġbottleneck , Ġwhile Ġtackling Ġthe Ġresearch Ġchallenges Ġthat Ġspeech Ġsynthesis Ġand Ġmachine Ġtranslation Ġpose Ġfor Ġsuch Ġlanguages Ġcould Ġlead Ġto Ġsuch Ġsystems Ġbeing Ġdeployed Ġto Ġserve Ġmore Ġunder - represented Ġcommunities . </s> The ĠMap ud ung un ĠLanguage </s> Map ud ung un Ġ( iso Ġ6 39 - 3 : Ġar n ) Ġis Ġan Ġindigenous Ġlanguage Ġof Ġthe ĠAmericas Ġspoken Ġnative ly Ġin ĠChile Ġand ĠArgentina , Ġwith Ġan Ġestimated Ġ100 Ġto Ġ200 Ġthousand Ġspeakers Ġin ĠChile Ġand Ġ27 Ġto Ġ60 Ġthousand Ġspeakers Ġin ĠArgentina ĠB IB REF 0 . ĠIt Ġis Ġan Ġisolate Ġlanguage Ġand Ġis Ġclassified Ġas Ġthreatened Ġby ĠEth n ologue , Ġhence Ġthe Ġcritical Ġimportance Ġof Ġall Ġdocumentary Ġefforts . ĠAlthough Ġthe Ġmorphology Ġof Ġnoun s Ġis Ġrelatively Ġsimple , ĠMap ud ung un Ġverb Ġmorphology Ġis Ġhighly Ġagg l ut in ative Ġand Ġcomplex . ĠSome Ġanalyses Ġprovide Ġas Ġmany Ġas Ġ36 Ġverb Ġsuffix Ġslots ĠB IB REF 1 . ĠA Ġtypical Ġcomplex Ġverb Ġform Ġoccurring Ġin Ġour Ġcorpus Ġof Ġspoken ĠMap ud ung un Ġconsists Ġof Ġfive Ġor Ġsix Ġmorp hem es . </s> Map ud ung un Ġhas Ġseveral Ġinteresting Ġgram matical Ġproperties . ĠIt Ġis Ġa Ġpoly sy nt hetic Ġlanguage Ġin Ġthe Ġsense Ġof ĠB IB REF 2 ; Ġsee ĠB IB REF 3 Ġfor Ġexplicit Ġargument ation . ĠAs Ġwith Ġother Ġpoly sy nt hetic Ġlanguages , ĠMap ud ung un Ġhas ĠN oun ĠInc orpor ation ; Ġhowever , Ġit Ġis Ġunique Ġinsofar Ġas Ġthe ĠN oun Ġappears Ġto Ġthe Ġright Ġof Ġthe ĠVerb , Ġinstead Ġof Ġto Ġthe Ġleft , Ġas Ġin Ġmost Ġpoly sy nt hetic Ġlanguages ĠB IB REF 4 . ĠOne Ġfurther Ġdistinction Ġof ĠMap ud ung un Ġis Ġthat , Ġwhereas Ġother Ġpoly sy nt hetic Ġlanguages Ġare Ġcharacterized Ġby Ġa Ġlack Ġof Ġinf init ives , ĠMap ud ung un Ġhas Ġinf init ival Ġverb Ġforms ; Ġthat Ġis , Ġwhile Ġsubordinate Ġclauses Ġin ĠMap ud ung un Ġclosely Ġresemble Ġpossessed Ġnom inals Ġand Ġmay Ġoccur Ġwith Ġan Ġanalytic Ġmarker Ġresembling Ġposs essor Ġagreement , Ġthere Ġis Ġno Ġagreement Ġinf lection Ġon Ġthe Ġverb Ġitself . ĠOne Ġfurther Ġremarkable Ġproperty Ġof ĠMap ud ung un Ġis Ġits Ġinverse Ġvoice Ġsystem Ġof Ġagreement , Ġwhereby Ġthe Ġhighest Ġagreement Ġis Ġwith Ġthe Ġargument Ġhighest Ġin Ġan Ġanim acy Ġhierarchy Ġregardless Ġof Ġthem atic Ġrole ĠB IB REF 5 . </s> The ĠResource </s> The Ġresource Ġis Ġcomprised Ġof Ġ142 Ġhours Ġof Ġspoken ĠMap ud ung un Ġthat Ġwas Ġrecorded Ġduring Ġthe ĠAV EN UE Ġproject ĠB IB REF 6 Ġin Ġ2001 Ġto Ġ2005 . ĠThe Ġdata Ġwas Ġrecorded Ġunder Ġa Ġpartnership Ġbetween Ġthe ĠAV EN UE Ġproject , Ġfunded Ġby Ġthe ĠUS ĠNational ĠScience ĠFoundation Ġat ĠCarnegie ĠMellon ĠUniversity , Ġthe ĠChilean ĠMinistry Ġof ĠEducation Ġ( M ined uc ), Ġand Ġthe ĠInstit uto Ġde ĠEst ud ios ĠInd ÃŃ gen as Ġat ĠUnivers idad Ġde ĠLa ĠFr on tera , Ġoriginally Ġspanning Ġ170 Ġhours Ġof Ġaudio . ĠWe Ġhave Ġrecently Ġcleaned Ġthe Ġdata Ġand Ġare Ġreleasing Ġit Ġpublicly Ġfor Ġthe Ġfirst Ġtime Ġ( although Ġit Ġhas Ġbeen Ġshared Ġwith Ġindividual Ġresearchers Ġin Ġthe Ġpast ) Ġalong Ġwith ĠN LP Ġbas elines . </s> The Ġrecordings Ġwere Ġtransc ribed Ġand Ġtranslated Ġinto ĠSpanish Ġat Ġthe ĠInstit uto Ġde ĠEst ud ios ĠInd ÃŃ gen as Ġat ĠUnivers idad Ġde ĠLa ĠFr on tera . ĠThe Ġcorpus Ġcovers Ġthree Ġdialect s Ġof ĠMap ud ung un : Ġabout Ġ110 Ġhours Ġof ĠNg ul uc he , Ġ20 Ġhours Ġof ĠLaf ken che Ġand Ġ10 Ġhours Ġof ĠPew en che . ĠThe Ġthree Ġdialect s Ġare Ġquite Ġsimilar , Ġwith Ġsome Ġminor Ġsemantic Ġand Ġphon etic Ġdifferences . ĠThe Ġfourth Ġtraditionally Ġdistinguished Ġdialect , ĠHu ill iche , Ġhas Ġseveral Ġgram matical Ġdifferences Ġfrom Ġthe Ġother Ġthree Ġand Ġis Ġclassified Ġby ĠEth n ologue Ġas Ġa Ġseparate Ġlanguage , Ġiso Ġ6 39 - 3 : Ġhuh , Ġand Ġas Ġnearly Ġextinct . </s> The Ġrecordings Ġare Ġrestricted Ġto Ġa Ġsingle Ġdomain : Ġprimary , Ġpreventive , Ġand Ġtreatment Ġhealth Ġcare , Ġincluding Ġboth ĠWestern Ġand ĠMap uc he Ġtraditional Ġmedicine . ĠThe Ġrecording Ġsessions Ġwere Ġconducted Ġas Ġinteractive Ġconversations Ġso Ġas Ġto Ġbe Ġnatural Ġin ĠMap uc he Ġculture , Ġand Ġthey Ġwere Ġopen - ended , Ġfollowing Ġan Ġethn ographic Ġapproach . ĠThe Ġinterviewer Ġwas Ġtrained Ġin Ġthese Ġmethods Ġalong Ġwith Ġthe Ġuse Ġof Ġthe Ġdigital Ġrecording Ġsystems Ġthat Ġwere Ġavailable Ġat Ġthe Ġtime . ĠWe Ġalso Ġfollowed Ġhuman Ġsubject Ġprotocol . ĠEach Ġperson Ġsigned Ġa Ġconsent Ġform Ġto Ġrelease Ġthe Ġrecordings Ġfor Ġresearch Ġpurposes Ġand Ġthe Ġdata Ġhave Ġbeen Ġaccordingly Ġanonym ized . ĠBecause ĠMach i Ġ( traditional ĠMap uc he Ġheal ers ) Ġwere Ġinterviewed , Ġwe Ġasked Ġthe Ġtransc rib ers Ġto Ġdelete Ġany Ġculturally Ġproprietary Ġknowledge Ġthat Ġa ĠMach i Ġmay Ġhave Ġrevealed Ġduring Ġthe Ġconversation . ĠSimilarly , Ġwe Ġdeleted Ġany Ġnames Ġor Ġany Ġinformation Ġthat Ġmay Ġidentify Ġthe Ġparticipants . </s> The Ġcorpus Ġis Ġculturally Ġrelevant Ġbecause Ġit Ġwas Ġcreated Ġby ĠMap uc he Ġpeople , Ġusing Ġtraditional Ġways Ġof Ġrelating Ġto Ġeach Ġother Ġin Ġconversations . ĠThey Ġdiscussed Ġpersonal Ġexperiences Ġwith Ġprimary Ġhealth Ġcare Ġin Ġthe Ġtraditional ĠMap uc he Ġsystem Ġand Ġthe ĠChilean Ġhealth Ġcare Ġsystem , Ġtalking Ġabout Ġillnesses Ġand Ġthe Ġway Ġthey Ġwere Ġcured . ĠThe Ġparticipants Ġranged Ġfrom Ġ16 Ġyears Ġold Ġto Ġ100 Ġyears Ġold , Ġalmost Ġin Ġequal Ġnumbers Ġof Ġmen Ġand Ġwomen , Ġand Ġthey Ġwere Ġall Ġnative Ġspeakers Ġof ĠMap ud ung un . </s> The ĠResource Ġ: :: ĠOrth ography </s> At Ġthe Ġtime Ġof Ġthe Ġcollection Ġand Ġtranscription Ġof Ġthe Ġcorpus , Ġthe Ġorth ography Ġof ĠMap ud ung un Ġwas Ġnot Ġstandardized . ĠThe ĠMap uc he Ġteam Ġat Ġthe ĠInstit uto Ġde ĠEst ud ios ĠInd ÃŃ gen as Ġ( IE I ĠâĢĵ ĠInstitute Ġfor ĠIndigenous ĠStudies ) Ġdeveloped Ġa Ġsupra - dial ect al Ġalphabet Ġthat Ġcomprises Ġ28 Ġletters Ġthat Ġcover Ġ32 Ġphones Ġused Ġin Ġthe Ġthree ĠMap ud ung un Ġvariants . ĠThe Ġmain Ġcriterion Ġfor Ġchoosing Ġal ph abetic Ġcharacters Ġwas Ġto Ġuse Ġthe Ġcurrent ĠSpanish Ġkeyboard Ġthat Ġwas Ġavailable Ġon Ġall Ġcomputers Ġin ĠChilean Ġoffices Ġand Ġschools . ĠThe Ġalphabet Ġused Ġthe Ġsame Ġletters Ġused Ġin ĠSpanish Ġfor Ġthose Ġphon em es Ġthat Ġsound Ġlike ĠSpanish Ġphon em es . ĠDi ac rit ics Ġsuch Ġas Ġapost rop hes Ġwere Ġused Ġfor Ġsounds Ġthat Ġare Ġnot Ġfound Ġin ĠSpanish . </s> As Ġa Ġresult , Ġcertain Ġorth ographic Ġconventions Ġthat Ġwere Ġmade Ġat Ġthe Ġtime Ġdev iate Ġfrom Ġthe Ġnow - standard Ġorth ography Ġof ĠMap ud ung un , ĠAz um che fe . ĠWe Ġplan Ġto Ġnormal ize Ġthe Ġorth ography Ġof Ġthe Ġcorpus , Ġand Ġin Ġfact Ġa Ġsmall Ġsample Ġhas Ġalready Ġbeen Ġconverted Ġto Ġthe Ġmodern Ġorth ography . ĠHowever , Ġwe Ġbelieve Ġthat Ġthe Ġoriginal Ġtranscript ions Ġwill Ġalso Ġbe Ġinvaluable Ġfor Ġacademic , Ġhistorical , Ġand Ġcultural Ġpurposes , Ġhence Ġwe Ġrelease Ġthe Ġcorpus Ġusing Ġthese Ġconventions . </s> The ĠResource Ġ: :: ĠAdditional ĠAnnotations </s> In Ġaddition , Ġthe Ġtranscription Ġincludes Ġannotations Ġfor Ġnoises Ġand Ġdis flu encies Ġincluding Ġaborted Ġwords , Ġmis pron unci ations , Ġpoor Ġintellig ibility , Ġrepeated Ġand Ġcorrected Ġwords , Ġfalse Ġstarts , Ġhes itations , Ġundefined Ġsound Ġor Ġpron unci ations , Ġnon - verbal Ġartic ulations , Ġand Ġpauses . ĠForeign Ġwords , Ġin Ġthis Ġcase ĠSpanish Ġwords , Ġare Ġalso Ġlabelled Ġas Ġsuch . </s> The ĠResource Ġ: :: ĠClean ing </s> The Ġdialog ues Ġwere Ġoriginally Ġrecorded Ġusing Ġa ĠSony ĠD AT Ġrecorder Ġ( 48 kHz ), Ġmodel ĠT CD - D 8 , Ġand ĠSony Ġdigital Ġstereo Ġmicrophone , Ġmodel ĠEC M - DS 70 P . ĠTrans cription Ġwas Ġperformed Ġwith Ġthe ĠTrans Edit Ġtranscription Ġtool Ġv . 1 . 1 Ġbeta Ġ10 , Ġwhich Ġsynchron izes Ġthe Ġtransc ribed Ġtext Ġand Ġthe Ġwave Ġfiles . </s> However , Ġwe Ġfound Ġthat Ġa Ġnon - t riv ial Ġnumber Ġof Ġthe Ġutter ance Ġboundaries Ġand Ġspeaker Ġannotations Ġwere Ġflawed . ĠAlso Ġsome Ġrecording Ġsessions Ġdid Ġnot Ġhave Ġa Ġcomplete Ġset Ġof Ġmatching Ġaudio , Ġtranscription , Ġand Ġtranslation Ġfiles . ĠHence , Ġin Ġan Ġeffort Ġto Ġprovide Ġa Ġrelatively ĠâĢ ľ clean " Ġcorpus Ġfor Ġmodern Ġcomputational Ġexperiments , Ġwe Ġconverted Ġthe Ġencoding Ġof Ġthe Ġtextual Ġtranscription Ġfrom ĠLatin - 1 Ġto ĠUnicode , ĠDOS Ġto ĠUN IX Ġline Ġendings , Ġa Ġnow Ġmore Ġstandard Ġtext Ġencoding Ġformat Ġthan Ġwhat Ġwas Ġused Ġwhen Ġthe Ġdata Ġwas Ġfirst Ġcollected . ĠAdditionally , Ġwe Ġrenamed Ġa Ġsmall Ġportion Ġof Ġfiles Ġwhich Ġhad Ġbeen Ġmis named Ġand Ġremoved Ġseveral Ġduplicate Ġfiles . </s> Although Ġall Ġof Ġthe Ġdata Ġwas Ġrecorded Ġwith Ġsimilar Ġequipment Ġin Ġrelatively Ġquiet Ġenvironments , Ġthe Ġac oust ics Ġare Ġnot Ġas Ġuniform Ġas Ġwe Ġwould Ġlike Ġfor Ġbuilding Ġspeech Ġsynthes izers . ĠThus Ġwe Ġapplied Ġstandardized Ġpower Ġnormal ization . ĠWe Ġalso Ġmoved Ġthe Ġboundaries Ġof Ġthe Ġturns Ġto Ġstandard ize Ġthe Ġamount Ġof Ġleading Ġand Ġtrailing Ġsilence Ġin Ġeach Ġturn . ĠThis Ġis Ġa Ġstandard Ġprocedure Ġfor Ġspeech Ġrecognition Ġand Ġsynthesis Ġdatasets . ĠFinally Ġwe Ġused Ġthe Ġtechniques Ġin ĠB IB REF 7 Ġfor Ġfound Ġdata Ġto Ġre - align Ġthe Ġtext Ġto Ġthe Ġaudio Ġand Ġfind Ġout Ġwhich Ġturns Ġare Ġbest Ġ( or Ġworst ) Ġaligned Ġso Ġthat Ġwe Ġcan Ġselect Ġsegments Ġthat Ġgive Ġthe Ġmost Ġaccurate Ġalign ments . ĠSome Ġof Ġthe Ġmis align ments Ġmay Ġin Ġpart Ġbe Ġdue Ġto Ġvaried Ġorth ography , Ġand Ġwe Ġintend , Ġbut Ġhave Ġnot Ġyet , Ġto Ġinvestigate Ġnormal ization Ġof Ġorth ography Ġ( i . e . Ġspelling Ġcorrection ) Ġto Ġmitigate Ġthis . </s> The ĠResource Ġ: :: ĠTraining , ĠDev , Ġand ĠTest ĠSpl its </s> We Ġcreate Ġtwo Ġtraining Ġsets , Ġone Ġappropriate Ġfor Ġsingle - spe aker Ġspeech Ġsynthesis Ġexperiments , Ġand Ġone Ġappropriate Ġfor Ġmultiple - spe aker Ġspeech Ġrecognition Ġand Ġmachine Ġtranslation Ġexperiments . ĠIn Ġboth Ġcases , Ġour Ġtraining , Ġdevelopment , Ġand Ġtest Ġsplits Ġare Ġperformed Ġat Ġthe Ġdialogue Ġlevel , Ġso Ġthat Ġall Ġexamples Ġfrom Ġeach Ġdialogue Ġbelong Ġto Ġexactly Ġone Ġof Ġthese Ġsets . </s> For Ġsingle - spe aker Ġspeech Ġsynthesis , Ġwe Ġonly Ġuse Ġthe Ġdialog Ġturns Ġof Ġthe Ġspeaker Ġwith Ġthe Ġlargest Ġvolume Ġof Ġdata Ġ( n ml ch ĠâĢĵ Ġone Ġof Ġthe Ġinterview ers ). ĠThe Ġtraining Ġset Ġincludes Ġ$ 221 . 8 $ Ġthousand Ġsentences Ġfrom Ġ285 Ġdialog ues , Ġwith Ġ12 Ġand Ġ46 Ġconversations Ġreserved Ġfor Ġthe Ġdevelopment Ġand Ġtest Ġset . </s> For Ġspeech Ġrecognition Ġexperiments , Ġwe Ġensure Ġthat Ġour Ġtest Ġset Ġincludes Ġunique Ġspeakers Ġas Ġwell Ġas Ġspeakers Ġthat Ġoverlap Ġwith Ġthe Ġtraining Ġset , Ġin Ġorder Ġto Ġallow Ġfor Ġcomparisons Ġof Ġthe Ġability Ġof Ġthe Ġspeech Ġrecognition Ġsystem Ġto Ġgeneral ize Ġover Ġseen Ġand Ġnew Ġspeakers . ĠFor Ġconsistency , Ġwe Ġuse Ġthe Ġsame Ġdataset Ġsplits Ġfor Ġthe Ġmachine Ġtranslation Ġexperiments . ĠThe Ġstatistics Ġin ĠTable Ġreflect Ġthis Ġsplit . </s> Applications </s> Our Ġresource Ġhas Ġthe Ġpotential Ġto Ġbe Ġthe Ġbasis Ġof Ġcomputational Ġresearch Ġin ĠMap ud ung un Ġacross Ġseveral Ġareas . ĠSince Ġthe Ġcollected Ġaudio Ġhas Ġbeen Ġtransc ribed , Ġour Ġresource Ġis Ġappropriate Ġfor Ġthe Ġstudy Ġof Ġautomatic Ġspeech Ġrecognition Ġand Ġspeech Ġsynthesis . ĠThe ĠSpanish Ġtranslations Ġenable Ġthe Ġcreation Ġof Ġmachine Ġtranslation Ġsystems Ġbetween ĠMap ud ung un Ġand ĠSpanish , Ġas Ġwell Ġas Ġend - to - end Ġ( or Ġdirect ) Ġspeech Ġtranslation . ĠWe Ġin Ġfact Ġbuilt Ġsuch Ġspeech Ġsynthesis , Ġspeech Ġrecognition , Ġand Ġmachine Ġtranslation Ġsystems Ġas Ġa Ġshowcase Ġof Ġthe Ġusefulness Ġof Ġour Ġcorpus Ġin Ġthat Ġresearch Ġdirection . </s> Furthermore , Ġour Ġannotations Ġof Ġthe ĠSpanish Ġwords Ġinter sp ersed Ġin ĠMap ud ung un Ġspeech Ġcould Ġallow Ġfor Ġa Ġstudy Ġof Ġcode - sw itching Ġpatterns Ġwithin Ġthe ĠMap uc he Ġcommunity . ĠIn Ġaddition , Ġour Ġannotations Ġof Ġnon - standard ized Ġorth ographic Ġtranscript ions Ġcould Ġbe Ġextremely Ġuseful Ġin Ġthe Ġstudy Ġof Ġhistorical Ġlanguage Ġand Ġorth ography Ġchange Ġas Ġa Ġlanguage Ġmoves Ġfrom Ġpredominantly Ġoral Ġto Ġbeing Ġwritten Ġin Ġa Ġstandardized Ġorth ography , Ġas Ġwell Ġas Ġin Ġbuilding Ġspelling Ġnormal ization Ġand Ġcorrection Ġsystems . ĠThe Ġrelatively Ġlarge Ġamount Ġof Ġdata Ġthat Ġwe Ġcollected Ġwill Ġalso Ġallow Ġfor Ġthe Ġtraining Ġof Ġlarge Ġlanguage Ġmodels , Ġwhich Ġin Ġturn Ġcould Ġbe Ġused Ġas Ġthe Ġbasis Ġfor Ġpredictive Ġkeyboards Ġtailored Ġto ĠMap ud ung un . ĠLast , Ġsince Ġall Ġdata Ġare Ġdialog ues Ġannot ated Ġfor Ġthe Ġdifferent Ġspeaker Ġturns , Ġthey Ġcould Ġbe Ġuseful Ġfor Ġbuilding ĠMap ud ung un Ġdialogue Ġsystems Ġand Ġchat bot - like Ġapplications . </s> The Ġpotential Ġapplications Ġof Ġour Ġresource , Ġhowever , Ġare Ġnot Ġexhausted Ġin Ġlanguage Ġtechnologies . ĠThe Ġresource Ġas Ġa Ġwhole Ġcould Ġbe Ġinvaluable Ġfor Ġethn ographic Ġand Ġsoc iological Ġresearch , Ġas Ġthe Ġconversations Ġcontrast Ġtraditional Ġand ĠWestern Ġmedicine Ġpractices , Ġand Ġthey Ġcould Ġreveal Ġinteresting Ġaspects Ġof Ġthe ĠMap uc he Ġculture . </s> In Ġaddition , Ġthe Ġcorpus Ġis Ġa Ġgold mine Ġof Ġdata Ġfor Ġstudying Ġthe Ġmorph ost ynt ax Ġof ĠMap ud ung un ĠB IB REF 8 . ĠAs Ġan Ġisolate Ġpoly sy nt hetic Ġlanguage , Ġthe Ġstudy Ġof ĠMap ud ung un Ġcan Ġprovide Ġinsights Ġinto Ġthe Ġrange Ġof Ġpossibilities Ġwithin Ġhuman Ġlanguages Ġcan Ġwork . </s> Bas eline ĠResults </s> Using Ġthe Ġaforementioned Ġhigher Ġquality Ġportions Ġof Ġthe Ġcorpus , Ġwe Ġtrained Ġbaseline Ġsystems Ġfor ĠMap ud ung un Ġspeech Ġrecognition Ġand Ġspeech Ġsynthesis , Ġas Ġwell Ġas ĠMachine ĠTranslation Ġsystems Ġbetween ĠMap ud ung un Ġand ĠSpanish . </s> Bas eline ĠResults Ġ: :: ĠSpeech ĠSynt hesis </s> In Ġour Ġprevious Ġwork Ġon Ġbuilding Ġspeech Ġsystems Ġon Ġfound Ġdata Ġin Ġ700 Ġlanguages , ĠB IB REF 7 , Ġwe Ġaddressed Ġalignment Ġissues Ġ( when Ġaudio Ġis Ġnot Ġsegment ed Ġinto Ġturn / sent ence Ġsized Ġchunks ) Ġand Ġcorrectness Ġissues Ġ( when Ġthe Ġaudio Ġdoes Ġnot Ġmatch Ġthe Ġtranscription ). ĠWe Ġused Ġthe Ġsame Ġtechniques Ġhere , Ġas Ġdescribed Ġabove . </s> For Ġthe Ġbest Ġquality Ġspeech Ġsynthesis Ġwe Ġneed Ġa Ġfew Ġhours Ġof Ġphon etically - balanced , Ġsingle - spe aker , Ġread Ġspeech . ĠOur Ġfirst Ġstep Ġwas Ġto Ġuse Ġthe Ġstart Ġand Ġend Ġpoints Ġfor Ġeach Ġturn Ġin Ġthe Ġdialog ues , Ġand Ġselect Ġthose Ġof Ġthe Ġmost Ġfrequent Ġspeaker , Ġn ml ch . ĠThis Ġgave Ġus Ġaround Ġ18 250 Ġsegments . ĠWe Ġfurther Ġautomatically Ġremoved Ġexcessive Ġsilence Ġfrom Ġthe Ġstart , Ġmiddle Ġand Ġend Ġof Ġthese Ġturns Ġ( based Ġon Ġoccurrence Ġof ĠF 0 ). ĠThis Ġgave Ġus Ġ13 Ġhours Ġand Ġ48 Ġminutes Ġof Ġspeech . </s> We Ġphon etically Ġaligned Ġthis Ġdata Ġand Ġbuilt Ġa Ġspeech Ġcluster gen Ġstatistical Ġspeech Ġsynthes izer ĠB IB REF 9 Ġfrom Ġall Ġof Ġthis Ġdata . ĠWe Ġres ynt hes ized Ġall Ġof Ġthe Ġdata Ġand Ġmeasured Ġthe Ġdifference Ġbetween Ġthe Ġsynthes ized Ġdata Ġand Ġthe Ġoriginal Ġdata Ġusing ĠMel ĠC ep st ral ĠDist ortion , Ġa Ġstandard Ġmethod Ġfor Ġautomatically Ġmeasuring Ġquality Ġof Ġspeech Ġgeneration ĠB IB REF 10 . ĠWe Ġthen Ġordered Ġthe Ġsegments Ġby Ġtheir Ġgeneration Ġscore Ġand Ġtook Ġthe Ġtop Ġ2000 Ġturns Ġto Ġbuild Ġa Ġnew Ġsynthes izer , Ġassuming Ġthe Ġbetter Ġscores Ġcorrespond ed Ġto Ġbetter Ġalign ments , Ġfollowing Ġthe Ġtechniques Ġof ĠB IB REF 7 . </s> The Ġinitial Ġbuild Ġgave Ġan ĠM CD Ġon Ġheld Ġout Ġdata Ġof Ġ6 . 483 . ĠWhile Ġthe Ġ2000 Ġbest Ġsegment Ġdataset Ġgives Ġan ĠM CD Ġof Ġ5 . 551 , Ġwhich Ġis Ġa Ġlarge Ġimprovement . ĠThe Ġquality Ġof Ġthe Ġgenerated Ġspeech Ġgoes Ġfrom Ġunderstandable , Ġonly Ġif Ġyou Ġcan Ġsee Ġthe Ġtext , Ġto Ġunderstandable , Ġand Ġtransc rib able Ġeven Ġfor Ġnon - Map ud ung un Ġspeakers . ĠWe Ġdo Ġnot Ġbelieve Ġwe Ġare Ġbuilding Ġthe Ġbest Ġsynthes izer Ġwith Ġour Ġcurrent Ġ( non - ne ural ) Ġtechniques , Ġbut Ġwe Ġdo Ġbelieve Ġwe Ġare Ġselecting Ġthe Ġbest Ġtraining Ġdata Ġfor Ġother Ġstatistical Ġand Ġneural Ġtraining Ġtechniques Ġin Ġboth Ġspeech Ġsynthesis Ġand Ġspeech Ġrecognition . </s> Bas eline ĠResults Ġ: :: ĠSpeech ĠRecogn ition </s> For Ġspeech Ġrecognition Ġ( AS R ) Ġwe Ġused ĠK aldi ĠB IB REF 11 . ĠAs Ġwe Ġdo Ġnot Ġhave Ġaccess Ġto Ġpronunciation Ġlex ica Ġfor ĠMap ud ung un , Ġwe Ġhad Ġto Ġapproximate Ġthem Ġwith Ġtwo Ġsettings . ĠIn Ġthe Ġfirst Ġsetting , Ġwe Ġmake Ġthe Ġsimple Ġassumption Ġthat Ġeach Ġcharacter Ġcorresponds Ġto Ġa Ġpron un ced Ġphon eme . ĠIn Ġthe Ġsecond Ġsetting , Ġwe Ġinstead Ġused Ġthe Ġgenerated Ġphon etic Ġlex icon Ġalso Ġused Ġin Ġthe Ġabove - mentioned Ġspeech Ġsynthesis Ġtechniques . ĠThe Ġtrain / dev / test Ġsplits Ġare Ġacross Ġconversations , Ġas Ġdescribed Ġabove . </s> Under Ġthe Ġfirst Ġsetting , Ġwe Ġobtained Ġa Ġ60 % Ġcharacter Ġerror Ġrate , Ġwhile Ġthe Ġgenerated Ġlex icon Ġsignificantly Ġboosts Ġperformance , Ġas Ġour Ġsystems Ġachieve Ġa Ġnotably Ġreduced Ġ30 % Ġphone Ġerror Ġrate . ĠNaturally , Ġthese Ġresults Ġare Ġrelatively Ġfar Ġfrom Ġthe Ġquality Ġof ĠAS R Ġsystems Ġtrained Ġon Ġlarge Ġamounts Ġof Ġclean Ġdata Ġsuch Ġas Ġthose Ġavailable Ġin ĠEnglish . ĠGiven Ġthe Ġquality Ġof Ġthe Ġrecordings , Ġand Ġthe Ġlack Ġof Ġadditional Ġresources , Ġwe Ġconsider Ġour Ġresults Ġfairly Ġreasonable Ġand Ġthey Ġwould Ġstill Ġbe Ġusable Ġfor Ġsimple Ġdialog - like Ġtasks . ĠWe Ġanticipate , Ġthough , Ġthat Ġone Ġcould Ġsignificantly Ġimprove ĠAS R Ġquality Ġover Ġour Ġdataset , Ġby Ġusing Ġin - domain Ġlanguage Ġmodels , Ġor Ġby Ġtraining Ġend - to - end Ġneural Ġrecogn izers Ġleveraging Ġlanguages Ġwith Ġsimilar Ġphon etic Ġinvent ories ĠB IB REF 12 Ġor Ġby Ġusing Ġthe Ġavailable ĠSpanish Ġtranslations Ġin Ġa Ġmulti - source Ġscenario ĠB IB REF 13 . </s> Bas eline ĠResults Ġ: :: ĠMap ud ung un âĢĵ Spanish ĠMachine ĠTranslation </s> We Ġbuilt Ġneural Ġend - to - end Ġmachine Ġtranslation Ġsystems Ġbetween ĠMap ud ung un Ġand ĠSpanish Ġin Ġboth Ġdirections , Ġusing Ġstate - of - the - art ĠTrans former Ġarchitecture ĠB IB REF 14 Ġwith Ġthe Ġtool kit Ġof ĠB IB REF 15 . ĠWe Ġtrain Ġour Ġsystems Ġat Ġthe Ġsub word Ġlevel Ġusing ĠByte - P air ĠEnc oding ĠB IB REF 16 Ġwith Ġa Ġvocabulary Ġof Ġ5000 Ġsub words , Ġshared Ġbetween Ġthe Ġsource Ġand Ġtarget Ġlanguages . ĠWe Ġuse Ġfive Ġlayers Ġfor Ġeach Ġof Ġthe Ġenc oder Ġand Ġthe Ġdec oder , Ġan Ġembed ding Ġsize Ġof Ġ512 , Ġfeed Ġforward Ġtransformation Ġsize Ġof Ġ2048 , Ġand Ġeight Ġattention Ġheads . ĠWe Ġuse Ġdrop out ĠB IB REF 17 Ġwith Ġ$ 0 . 4 $ Ġprobability Ġas Ġwell Ġas Ġlabel Ġsmoot hing Ġset Ġto Ġ$ 0 . 1 $. ĠWe Ġtrain Ġwith Ġthe ĠAdam Ġoptim izer ĠB IB REF 18 Ġfor Ġup Ġto Ġ200 Ġepoch s Ġusing Ġlearning Ġdecay Ġwith Ġa Ġpatience Ġof Ġsix Ġepoch s . </s> The Ġbaseline Ġresults Ġusing Ġdifferent Ġportions Ġof Ġthe Ġtraining Ġset Ġ( 10 k , Ġ50 k , Ġ100 k , Ġand Ġall Ġ( 220 k ) Ġparallel Ġsentences ) Ġon Ġboth Ġtranslation Ġdirections Ġare Ġpresented Ġin ĠTable Ġ, Ġusing Ġdet oken ized ĠB LE U ĠB IB REF 19 Ġ( a Ġstandard ĠMT Ġmetric ) Ġand Ġch r F ĠB IB REF 20 Ġ( a Ġmetric Ġthat Ġwe Ġconsider Ġto Ġbe Ġmore Ġappropriate Ġfor Ġpoly sy nt hetic Ġlanguages , Ġas Ġit Ġdoes Ġnot Ġrely Ġon Ġword Ġn - gram s ) Ġcomputed Ġwith Ġthe Ġsac re BLE U Ġtool kit ĠB IB REF 21 . ĠIt Ġit Ġworth Ġnoting Ġthe Ġdifference Ġin Ġquality Ġbetween Ġthe Ġtwo Ġdirections , Ġwith Ġtranslation Ġinto ĠSpanish Ġreaching Ġ20 . 4 Ġ( almost Ġ21 ) ĠB LE U Ġpoints Ġin Ġthe Ġdevelopment Ġset , Ġwhile Ġthe Ġopposite Ġdirection Ġ( trans l ating Ġinto ĠMap ud ung un ) Ġshows Ġabout Ġa Ġ7 ĠB LE U Ġpoints Ġworse Ġperformance . ĠThis Ġis Ġmost Ġlikely Ġdue Ġto ĠMap ud ung un Ġbeing Ġa Ġpoly sy nt hetic Ġlanguage , Ġwith Ġits Ġcomplicated Ġmorphology Ġposing Ġa Ġchallenge Ġfor Ġproper Ġgeneration . </s> Related ĠWork </s> Map ud ung un Ġgrammar Ġhas Ġbeen Ġstudied Ġsince Ġthe Ġarrival Ġof ĠEuropean Ġmissionaries Ġand Ġcolon izers Ġhundreds Ġof Ġyears Ġago . ĠMore Ġrecent Ġdescriptions Ġof ĠMap ud ung un Ġgrammar ĠB IB REF 1 Ġand ĠB IB REF 0 Ġinformed Ġthe Ġcollection Ġof Ġthe Ġresource Ġthat Ġwe Ġare Ġpresenting Ġin Ġthis Ġpaper . </s> Port ions Ġof Ġour Ġresource Ġhave Ġbeen Ġused Ġin Ġearly Ġefforts Ġto Ġbuild Ġlanguage Ġsystems Ġfor ĠMap ud ung un . ĠIn Ġparticular , ĠB IB REF 22 Ġfocused Ġon ĠMap ud ung un Ġmorphology Ġin Ġorder Ġto Ġcreate Ġspelling Ġcorrection Ġsystems , Ġwhile ĠB IB REF 23 , ĠB IB REF 6 , ĠB IB REF 24 , Ġand ĠB IB REF 25 Ġdeveloped Ġhybrid Ġrule - Ġand Ġphrase - based ĠStatistical ĠMachine ĠTranslation Ġsystems . </s> Naturally , Ġsimilar Ġworks Ġin Ġcollecting Ġcorpor a Ġin ĠIndigenous Ġlanguages Ġof ĠLatin ĠAmerica Ġare Ġabundant , Ġbut Ġvery Ġfew , Ġif Ġany , Ġhave Ġthe Ġscale Ġand Ġpotential Ġof Ġour Ġresource Ġto Ġbe Ġuseful Ġin Ġmany Ġdownstream Ġlanguage - specific Ġand Ġinter - disciplinary Ġapplications . ĠA Ġgeneral Ġoverview Ġof Ġthe Ġstate Ġof ĠN LP Ġfor Ġthe Ġunder - represented Ġlanguages Ġof Ġthe ĠAmericas Ġcan Ġbe Ġfound Ġat ĠB IB REF 26 . ĠTo Ġname Ġa Ġfew Ġof Ġthe Ġmany Ġnotable Ġworks , ĠB IB REF 27 Ġcreated Ġa Ġparallel ĠMix tec - Spanish Ġcorpus Ġfor ĠMachine ĠTranslation Ġand ĠB IB REF 28 Ġcreated Ġlex ical Ġresources Ġfor ĠA rap aho , Ġwhile ĠB IB REF 29 Ġand ĠB IB REF 30 Ġfocused Ġon Ġbuilding Ġspeech Ġcorpor a Ġfor ĠSouthern ĠQue ch ua Ġand ĠChat ino Ġrespectively . </s> Conclusion </s> With Ġthis Ġwork Ġwe Ġpresent Ġa Ġresource Ġthat Ġwill Ġbe Ġextremely Ġuseful Ġfor Ġbuilding Ġlanguage Ġsystems Ġin Ġan Ġendangered , Ġunder - represented Ġlanguage , ĠMap ud ung un . ĠWe Ġbenchmark ĠN LP Ġsystems Ġfor Ġspeech Ġsynthesis , Ġspeech Ġrecognition , Ġand Ġmachine Ġtranslation , Ġproviding Ġstrong Ġbaseline Ġresults . ĠThe Ġsize Ġof Ġour Ġresource Ġ( 142 Ġhours , Ġmore Ġthan Ġ260 k Ġtotal Ġsentences ) Ġhas Ġthe Ġpotential Ġto Ġalleviate Ġmany Ġof Ġthe Ġissues Ġfaced Ġwhen Ġbuilding Ġlanguage Ġtechnologies Ġfor ĠMap ud ung un , Ġin Ġcontrast Ġto Ġother Ġindigenous Ġlanguages Ġof Ġthe ĠAmericas Ġthat Ġunfortunately Ġremain Ġlow - resource . </s> Our Ġresource Ġcould Ġalso Ġbe Ġused Ġfor Ġethn ographic Ġand Ġanthrop ological Ġresearch Ġinto Ġthe ĠMap uc he Ġculture , Ġand Ġhas Ġthe Ġpotential Ġto Ġcontribute Ġto Ġinter cultural Ġbilingual Ġeducation , Ġpreservation Ġactivities Ġand Ġfurther Ġgeneral Ġadvancement Ġof Ġthe ĠMap ud ung un - speaking Ġcommunity . </s> Acknowled gements </s> The Ġdata Ġcollection Ġdescribed Ġin Ġthis Ġpaper Ġwas Ġsupported Ġby ĠNS F Ġgrants ĠI IS - 01 216 31 Ġ( AV EN UE ) Ġand ĠI IS - 05 34 217 Ġ( LE TR AS ), Ġwith Ġsupplemental Ġfunding Ġfrom ĠNS F 's ĠOffice Ġof ĠInternational ĠScience Ġand ĠEducation . ĠPrel iminary Ġfunding Ġfor Ġwork Ġon ĠMap ud ung un Ġwas Ġalso Ġprovided Ġby ĠDAR PA ĠThe Ġexperimental Ġmaterial Ġis Ġbased Ġupon Ġwork Ġgenerously Ġsupported Ġby Ġthe ĠNational ĠScience ĠFoundation Ġunder Ġgrant Ġ176 15 48 .