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Speech Emotion Recognition using Time-frequency Random Circular Shift and Deep Neural Networks

hal.structure.identifierInformatique, BioInformatique, Systèmes Complexes [IBISC]
dc.contributor.authorXIA, Sylvain
hal.structure.identifierInformatique, BioInformatique, Systèmes Complexes [IBISC]
dc.contributor.authorFOURER, Dominique
hal.structure.identifierLaboratoire de l'intégration, du matériau au système [IMS]
dc.contributor.authorAUDIN, Liliana
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorROUAS, Jean-Luc
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorSHOCHI, Takaaki
dc.date.accessioned2022-03-07T14:26:26Z
dc.date.available2022-03-07T14:26:26Z
dc.date.conference2022-05-23
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/129775
dc.description.abstractEnThis paper addresses the problem of emotion recognition from a speech signal. Thus, we investigate a data augmentation technique based on circular shift of the input time-frequency representation which significantly enhances the emotion prediction results using a deep convolutional neural network method. After an investigation of the best combination of the method parameters, we comparatively assess several neural network architectures (Alexnet, Resnet and Inception) using our approach applied on two publicly available datasets: eNTERFACE05 and EMO-DB. Our results reveal an improvement of the prediction accuracy in comparison to a more complicated technique of the state of the art based on Discriminant Temporal Pyramid Matching (DCNN-DTPM).
dc.language.isoen
dc.subject.enSpeech Emotion Recognition (SER)
dc.subject.enDeep Convolutional Neural Networks
dc.subject.enTime-frequency
dc.subject.enRandom Circular Shift (RCS)
dc.title.enSpeech Emotion Recognition using Time-frequency Random Circular Shift and Deep Neural Networks
dc.typeCommunication dans un congrès avec actes
dc.subject.halInformatique [cs]/Son [cs.SD]
dc.subject.halInformatique [cs]/Traitement du signal et de l'image
dc.subject.halInformatique [cs]/Intelligence artificielle [cs.AI]
bordeaux.hal.laboratoriesCLLE Montaigne : Cognition, langues, Langages, Ergonomie - UMR 5263*
bordeaux.institutionUniversité Bordeaux Montaigne
bordeaux.countryPT
bordeaux.title.proceedingSpeech Prosody 2022
bordeaux.conference.cityLisbonne
bordeaux.peerReviewedoui
hal.identifierhal-03583535
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03583535v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=XIA,%20Sylvain&FOURER,%20Dominique&AUDIN,%20Liliana&ROUAS,%20Jean-Luc&SHOCHI,%20Takaaki&rft.genre=proceeding


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