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时间:2020-04-27
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1、JournalofSoutheastUniversity(EnglishEdition)Vol.23,No.2,pp.174-178June2007ISSN1003—798DiscriminativetonemodeltrainingandoptimalintegrationforMandarinspeechrecognitionHuangHaoZhuJie(DepartmentofElectronicEngineering,ShanghaiJiaotongUniversity,Shanghai200240,China)Abstract:Twodiscriminativemethodsfors
2、olvingtoneproblemsinMandarinspeechrecognitionarepresented.First,discriminativetrainingontheHMM(hiddenMarkovmodel)basedtonemodelsisproposed.Thenanintegrationtechnigueoftonemodelsintoalargevocabularycontinuousspeechrecognitionsystemispresented.Discriminativemodelweighttrainingbasedonminimumphoneerrorc
3、riteriaisadoptedaimingatoptimalintegrationofthetonemodels.TheextendedBaumWelchalgorithmisappliedtofindthemodel-dependentweightstoscaletheacousticscoresandtonescores.Experimentalresultsshowthattonerecognitionratesandcontinuousspeechrecognitionaccuracycanbeimprovedbythediscriminativelytrainedtonemodel
4、.PerformanceofalargevocabularycontinuousMandarinspeechrecognitionsystemcanbefurtherenhancedbythediscriminativelytrainedweightcombinationsduetoabetterinterpolationofthegivenmodels.Keywords:discriminativetraining;minimumphoneerror;tonemodeling;MandarinspeechrecognitionTonerecognitionisanimportanttaskf
5、orManda-tonemodels,weproposeamethodtodiscriminativelyrinspeechrecognitionduetothetonalnatureoftheintegratetoneinformationintotheexistingsystems.Inlanguage.Therehasbeenmuchworkdonediscussingpreviousworkontonemodelingincorporationsuchastonemodelingtoimprovetonerecognitionaccuracy.inRefs.[23],aglobalac
6、ousticandtonemodelThemostpopularlyappliedistheframeandtheHMMweightiscommonlyappliedwhichmaynotobtainan[1][8]basedapproach.Inaddition,otherapproachessuchasoptimalresult.Liuetal.proposedanMCEbasedthestochasticpolynomialtonemodel(SPTM)pro-streamweightoptimiZationforaudio-visualLVSCR.posedinRef.[2]andth
7、edecisiontreebasedtoneInspiredbythiswork,weproposediscriminativetrain-[3]modelhavealsobeenproposed.ingonmodelweightusingtheEBalgorithmundertheInstate-of-the-artspeechrecognitionsystems,dis-MPEcriterio
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