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基于端点检测的语音分割方法.docx

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基于端点检测的语音分割方法 Title:EndpointDetection-BasedSpeechSegmentationMethods Abstract: Speechsegmentationisacrucialstepinvariousapplicationsofspeechprocessing,suchasspeakerdiarization,automaticspeechrecognition,andaudioindexing.Thetaskinvolvesdividingacontinuousaudiostreamintosmallersegmentsthatcanbeseparatelyprocessed.Thispaperfocusesonendpointdetection-basedspeechsegmentationmethods,whichrelyonidentifyingthebeginningandendofspeechsegmentsbasedonthecharacteristicsoftheaudiosignal. 1.Introduction Speechsegmentationplaysafundamentalroleinspeechprocessingtasks,asithelpsinisolatingandprocessingspeechsegmentsindividually.Inrecentyears,endpointdetection-basedmethodshavegainedsignificantattentionduetotheireffectivenessandlowcomputationalcomplexity.Thispaperaimstoprovideanoverviewofdifferentendpointdetectionmethodsandtheirapplicationsinspeechsegmentation. 2.Background 2.1SpeechSegmentationTechniques -Energy-basedapproaches -Zero-crossingrate-basedapproaches -Pitch-basedapproaches -Spectral-basedapproaches 2.2EndpointDetection -Importanceofendpointdetectioninspeechsegmentation -Characteristicsofspeechandnon-speechregions -Challengesandlimitationsinendpointdetection 3.EndpointDetection-BasedSpeechSegmentationMethods 3.1Energy-basedMethods -Short-termEnergyEndpointDetection -Long-TermEnergyEndpointDetection 3.2Zero-crossingRateMethods -SimpleZero-crossingRateEndpointDetection -DynamicZero-crossingRateEndpointDetection 3.3Pitch-basedMethods -Autocorrelation-basedEndpointDetection -HarmonicProductSpectrumEndpointDetection 3.4Spectral-basedMethods -Mel-FrequencyCepstralCoefficientsEndpointDetection -HiddenMarkovModelsEndpointDetection 4.EvaluationMetricsforSpeechSegmentation -SegmentalSignal-to-NoiseRatio(SNRseg) -Speech/Non-speechErrorRate(SNER) -SpeechActivityDetectionErrorRate(SADer) 5.ApplicationsofEndpointDetection-BasedSpeechSegmentation 5.1SpeakerDiarization 5.2AutomaticSpeechRecognition 5.3AudioIndexingandRetrieval 6.ChallengesandFutureDirections -Overcomingenvironmentalnoiseand