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1、DetectionofNegativeEmotionsinSpeechSignalsUsingBags-of-Audio-WordsFlorianB.Pokorny1;2;3;4,FranzGraf1;5,FranzPernkopf3;5,BjornW.Schuller¨1;6;71InstituteforInformationandCommunicationTechnologies,JoanneumResearchForschungsgesellschaftmbH,Graz,Austria2MachineIntelligence&Si
2、gnalProcessinggroup,MMK,TechnischeUniversitatM¨unchen,Germany¨3Brain,Ears&Eyes–PatternRecognitionInitiative(BEE-PRI),BioTechMed-Graz,Austria4InstituteofPhysiology,MedicalUniversityofGraz,Austria5SignalProcessingandSpeechCommunicationLaboratory,GrazUniversityofTechnology,
3、Austria6ChairofComplex&IntelligentSystems,UniversityofPassau,Germany7MachineLearningGroup,DepartmentofComputing,ImperialCollegeLondon,UKEmail:florian.pokorny@medunigraz.atAbstract—Boostedbyawidepotentialapplicationspectrum,Similartothefieldofautomaticspeechrecognition(cf.[
4、5]),emotionalspeechrecognition,i.e.,theautomaticcomputer-aidedarchitecturalstagesoffeatureextraction,featureprocessing,identificationofhumanemotionalstatesbasedonspeechsignals,andclassificationplayanimportantroleinemotionalspeechcurrentlydescribesapopularfieldofresearch.How
5、ever,avarietyrecognition.Moreover,forbothfieldstheneedofasuitableofstudiesespeciallyconcentratingontherecognitionofnegativeemotionsoftenneglectedthespecificrequirementsofreal-worldspeechcorpuswithappropriateannotationsfortrainingandscenarios,forexample,robustness,real-time
6、capability,andtestingthesystemisofimmensevalue.Asemotionscannotberealisticspeechcorpora.measuredbyobjectivemeans,especiallyemotionrecognitionMotivatedbythesefacts,arobust,low-complexclassificationsystemshavetodealwithambiguitiesalreadyinthedevel-systemforthedetectionofneg
7、ativeemotionsinspeechsignalsopmentphase,causedbydifferentannotators’interpretationswasimplementedonthebasisofaspontaneous,stronglyemotion-allycoloredspeechcorpus.Therefore,aninnovativeapproachinofemotions.Hence,inthefirstplace,emotionrecognitionthefieldofemotionrecognition
8、wasappliedasthecoreofthesystemscanneverachieveabetterperformancethanthesystem–thebag-of-wordsapproachth
