2023年全國碩士研究生考試考研英語一試題真題（含答案詳解+作文范文）_第1頁
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1、AResearchonSpeakerRecognitionAlgithmSpeakerIdentificationSystemImplementationMasterStudent:YANGShanxiSupervis:HUANGHanmingSpecialField:ComputerApplicationTechnologyGrade:2007Interests:SignalProcessingSpeechRecognitionAbs

2、tractSpeakerrecognitionisthemostnaturalbiometricidentificationtechnique.Itcanbedividedintotwosubfields:speakeridentificationspeakerverification.Speakerrecognitionautomaticallyidentifiesthespeakeraccdingtotheacteristicsem

3、bodiedinthespeakersspeechsignalsthekeyissuesarethechoiceofacteristicparametersrecognitionmodeling.Atpresentlinearpredictivecoding(LPC)parametersLPCcepstrum(LPCC)melfrequencycepstralcoefficients(MFCC)soonareoftenusedasfea

4、tureparametersinspeakerrecognitionthepopularrecognitionmodelaredynamictimewarping(DTW)vectquantization(VQ)hiddenMarkovmodel(HMM)etc.LPCCrepresentsthephysiologicaldifferencesofspeaker’svocaltractMFCCutilizesthenonlinearfr

5、equencyacteristicsofauditysystemthespeechperceptionacteristicsofhumanaudity.HilbertHuangTransfm(HHT)isproposedat1998duetoitsstrongadaptivetimevarianceprocessingcapabilityfnonsteadynonlinearsignalsHHTrapidlyreceiveswideat

6、tentionsgetmanysuccessfulapplicationsinsignalprocessingfield.HHTisalsothenewlymeasureofspeechsignalprocessing.Eachofabovespeechspeakerfeatures:LPCCMFCCHHThasitsownadvantagethoughsolelybeingappliedisfartoenoughtodescribet

7、hespeakersdiscriminativeacteristics.Eachofthesefeaturesmaycontainsemanticinfmationalsospeakeracteristicsintegralutilizationofthesesdiversefeaturesmaybethebestwaytoconstructareliablespeakerrecognitionsystem.Basinguponabov

8、eanalysisintheexperimentationofspeakerrecognitionLPCCMFCCHHTarerespectivelysuppliedtothespeakerrecognitionsystemthenthecombinedfeaturesofMFCCHHTareused.InexperimentsofthisthesisMatlabisthedevelopmentenvironment.LPCCMFCCH

9、HTcombinedfeaturesareextractedfmulatedfromspeechsignalthensuppliedtoseveralpopularmodels:DynamicTimeWarping(DTW)HiddenMarkovModel(HMM)GaussianMixtureModel(GMM).InadditiontheGaussiancomponentsinGMMarealsotestedfcomparingr

10、ecognitionperfmance.TheresultsshowedthatfspeakeridentificationHHTfeatureshavebetterrecognitionratethanLPCCMFCCwithcombinedfeaturesGMMisfavabletoDTWDHMMcombinedfeaturesissuperitoanynoncombinedfeatures:LPCCMFCCHHTfeature.I

11、thasbeenshownthatHHTfeaturecanbeusedasnewparametersinspeakerrecognitionifitiscombinedwithMFCCfeaturetofmulatecombinedonesthecombinedfeaturesmaysimultaneouslycontainMFCCdynamictimeacteristicsHHThighfrequencyresolutioncapa

12、bility.Thecombinedfeaturesmayimprovethesystemperfmance.GMMmaybethebestrecognitionmodelinspeakeridentificationsystem.Keywds:SpeakerIdentificationHiddenMarkovModel(HMM)HilbertHuangTransfm(HHT)MelFrequencyCeptralCoefficient

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