{"title":"面向人工翻译指导的ASR系统语言发现","authors":"Sebastian Stüker, A. Waibel","doi":"10.21437/Interspeech.2009-765","DOIUrl":null,"url":null,"abstract":"ABSTRACTNatural language processing systems, e.g for AutomaticSpeech Recognition (ASR) or Machine Translation (MT),havebeenstudiedonlyforafractionoftheapprox.7000lan-guagesthatexistintoday’sworld,themajorityofwhichhaveonlycomparativelyfewspeakersandfewresources.Thetra-ditionalapproachofcollectingandannotatingthenecessarytrainingdataisduetoeconomicconstraintsnotfeasibleformostofthem. AtthesametimeitisofvitalinteresttohaveNLPsystemsaddresspracticallyalllanguagesintheworld.New,efficientwaysofgatheringtheneededtrainingmaterialhavetobefound.InthispaperweproposeanewtechniqueofcollectingsuchdatabyexploitingtheknowledgegainedfromHumansimultaneoustranslationsthathappenfrequentlyintherealworld. Toshowthefeasibilityofourapproachwepresentfirstexperimentstowardsconstructingapronuncia-tiondictionaryfromthedatagained.Index Terms — Automatic Speech Recognition, Lan-guage Discovery, Machine Translation, Under-ResourcedLanguages1. INTRODUCTION1.1. The Traditional Way to Acquire Training DataTraining large vocabulary continuous speech recognition(LVCSR) systems requires a number resources in the tar-getedlanguage. Fortrainingtheacousticmodelofarecog-nitionsystemlargeamountsoftranscribedaudiorecordingsofspeechareneeded.Thetrainingofthelanguagemodelre-quireslargeamountsofwrittentextinthetargetedlanguage.Whenusingphonemebasedacousticmodels,apronunciationdictionaryisneededthatmapsthewrittenrepresentationofawordtothesequenceofitsphonemeswhenbeingspoken.Approximately7,000languagesexisttoday,thecurrenteditionofEthnologue[1]lists7,299.Sofar,automaticspeechrecognition (ASR) systems and machine translation (MT)","PeriodicalId":190269,"journal":{"name":"Workshop on Spoken Language Technologies for Under-resourced Languages","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"23","resultStr":"{\"title\":\"Towards human translations guided language discovery for ASR systems\",\"authors\":\"Sebastian Stüker, A. Waibel\",\"doi\":\"10.21437/Interspeech.2009-765\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACTNatural language processing systems, e.g for AutomaticSpeech Recognition (ASR) or Machine Translation (MT),havebeenstudiedonlyforafractionoftheapprox.7000lan-guagesthatexistintoday’sworld,themajorityofwhichhaveonlycomparativelyfewspeakersandfewresources.Thetra-ditionalapproachofcollectingandannotatingthenecessarytrainingdataisduetoeconomicconstraintsnotfeasibleformostofthem. AtthesametimeitisofvitalinteresttohaveNLPsystemsaddresspracticallyalllanguagesintheworld.New,efficientwaysofgatheringtheneededtrainingmaterialhavetobefound.InthispaperweproposeanewtechniqueofcollectingsuchdatabyexploitingtheknowledgegainedfromHumansimultaneoustranslationsthathappenfrequentlyintherealworld. Toshowthefeasibilityofourapproachwepresentfirstexperimentstowardsconstructingapronuncia-tiondictionaryfromthedatagained.Index Terms — Automatic Speech Recognition, Lan-guage Discovery, Machine Translation, Under-ResourcedLanguages1. INTRODUCTION1.1. The Traditional Way to Acquire Training DataTraining large vocabulary continuous speech recognition(LVCSR) systems requires a number resources in the tar-getedlanguage. Fortrainingtheacousticmodelofarecog-nitionsystemlargeamountsoftranscribedaudiorecordingsofspeechareneeded.Thetrainingofthelanguagemodelre-quireslargeamountsofwrittentextinthetargetedlanguage.Whenusingphonemebasedacousticmodels,apronunciationdictionaryisneededthatmapsthewrittenrepresentationofawordtothesequenceofitsphonemeswhenbeingspoken.Approximately7,000languagesexisttoday,thecurrenteditionofEthnologue[1]lists7,299.Sofar,automaticspeechrecognition (ASR) systems and machine translation (MT)\",\"PeriodicalId\":190269,\"journal\":{\"name\":\"Workshop on Spoken Language Technologies for Under-resourced Languages\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Workshop on Spoken Language Technologies for Under-resourced Languages\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21437/Interspeech.2009-765\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on Spoken Language Technologies for Under-resourced Languages","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21437/Interspeech.2009-765","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Towards human translations guided language discovery for ASR systems
ABSTRACTNatural language processing systems, e.g for AutomaticSpeech Recognition (ASR) or Machine Translation (MT),havebeenstudiedonlyforafractionoftheapprox.7000lan-guagesthatexistintoday’sworld,themajorityofwhichhaveonlycomparativelyfewspeakersandfewresources.Thetra-ditionalapproachofcollectingandannotatingthenecessarytrainingdataisduetoeconomicconstraintsnotfeasibleformostofthem. AtthesametimeitisofvitalinteresttohaveNLPsystemsaddresspracticallyalllanguagesintheworld.New,efficientwaysofgatheringtheneededtrainingmaterialhavetobefound.InthispaperweproposeanewtechniqueofcollectingsuchdatabyexploitingtheknowledgegainedfromHumansimultaneoustranslationsthathappenfrequentlyintherealworld. Toshowthefeasibilityofourapproachwepresentfirstexperimentstowardsconstructingapronuncia-tiondictionaryfromthedatagained.Index Terms — Automatic Speech Recognition, Lan-guage Discovery, Machine Translation, Under-ResourcedLanguages1. INTRODUCTION1.1. The Traditional Way to Acquire Training DataTraining large vocabulary continuous speech recognition(LVCSR) systems requires a number resources in the tar-getedlanguage. Fortrainingtheacousticmodelofarecog-nitionsystemlargeamountsoftranscribedaudiorecordingsofspeechareneeded.Thetrainingofthelanguagemodelre-quireslargeamountsofwrittentextinthetargetedlanguage.Whenusingphonemebasedacousticmodels,apronunciationdictionaryisneededthatmapsthewrittenrepresentationofawordtothesequenceofitsphonemeswhenbeingspoken.Approximately7,000languagesexisttoday,thecurrenteditionofEthnologue[1]lists7,299.Sofar,automaticspeechrecognition (ASR) systems and machine translation (MT)
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