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时间:2019-09-16
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1、中文3276字附录英文原文:ChineseJournalofElectronicsVo1.15,No.3,July2006ASpeaker--IndependentContinuousSpeechRecognitionSystemUsingBiomimeticPatternRecognitionWANGShoujueandQINHong(LaboratoryofArtificialNeuralNetworks,InstituteolSemiconductors,ChineseAcademySciences,Beijing100083,China)Abstract—In
2、speaker-independentspeechrecognition,thedisadvantageofthemostdiffusedtechnology(HMMs,orHiddenMarkovmodels)isnotonlytheneedofmanymoretrainingsamples,butalsolongtraintimerequirement.ThisPaperdescribestheuseofBiomimeticpatternrecognition(BPR)inrecognizingsomemandarincontinuousspeechinaspea
3、ker-independentManner.Aspeechdatabasewasdevelopedforthecourseofstudy.Thevocabularyofthedatabaseconsistsof15Chinesedish’snames,thelengthofeachnameis4Chinesewords.Neuralnetworks(NNs)basedonMulti-weightneuron(MWN)modelareusedtotrainandrecognizethespeechsounds.ThenumberofMWNwasinvestigatedt
4、oachievetheoptimalperformanceoftheNNs-basedBPR.Thissystem,whichisbasedonBPRandcancarryoutrealtimerecognitionreachesarecognitionrateof98.14%forthefirstoptionand99.81%forthefirsttwooptionstothePersonsfromdifferentprovincesofChinaspeakingcommonChinesespeech.Experimentswerealsocarriedontoev
5、aluateContinuousdensityhiddenMarkovmodels(CDHMM),Dynamictimewarping(DTW)andBPRforspeechrecognition.TheExperimentresultsshowthatBPRoutperformsCDHMMandDTWespeciallyinthecasesofsamplesofafinitesize.Keywords—Biomimeticpatternrecognition,Speechrecogniton,HiddenMarkovmodels(HMMs),Dynamictimew
6、arping(DTW).I.IntroductionThemaingoalofAutomaticspeechrecognition(ASR)istoproduceasystemwhichwillrecognizeaccuratelynormalhumanspeechfromanyspeaker.Therecognitionsystemmaybeclassifiedasspeaker-dependentorspeaker-independent.Thespeakerdependencerequiresthatthesystembepersonallytrainedwit
7、hthespeechofthepersonthatwillbeinvolvedwithitsoperationinordertoachieveahighrecognitionrate.Forapplicationsonthepublicfacilities,ontheotherhand,thesystemmustbecapableofrecognizingthespeechutteredbymanydifferentpeople,withdifferentgender,age,accent,etc.,thespeakerindependencehas
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