Speech emotion representation : A method to convert discrete to dimensional emotional models for emotional inference multimodal frameworks

Autores
Elkfury, Fernando; Ierache, Jorge Salvador
Año de publicación
2021
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Computer-Human interaction is more frequent now than ever before, thus the main goal of this research area is to improve communication with computers, so it becomes as natural as possible. A key aspect to achieve such interaction is the affective component often missing from last decade developments. To improve computer human interaction in this paper we present a method to convert discrete or categorical data from a CNN emotion classifier trained with Mel scale spectrograms to a two-dimensional model, pursuing integration of the human voice as a feature for emotional inference multimodal frameworks. Lastly, we discuss preliminary results obtained from presenting audiovisual stimuli to different subject and comparing dimensional arousal-valence results and it’s SAM surveys.
Facultad de Informática
Materia
Ciencias Informáticas
Emotions
Multimodal Framework
Affective computing
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/125145
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/125145
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Speech emotion representation : A method to convert discrete to dimensional emotional models for emotional inference multimodal frameworksElkfury, FernandoIerache, Jorge SalvadorCiencias InformáticasEmotionsMultimodal FrameworkAffective computingComputer-Human interaction is more frequent now than ever before, thus the main goal of this research area is to improve communication with computers, so it becomes as natural as possible. A key aspect to achieve such interaction is the affective component often missing from last decade developments. To improve computer human interaction in this paper we present a method to convert discrete or categorical data from a CNN emotion classifier trained with Mel scale spectrograms to a two-dimensional model, pursuing integration of the human voice as a feature for emotional inference multimodal frameworks. Lastly, we discuss preliminary results obtained from presenting audiovisual stimuli to different subject and comparing dimensional arousal-valence results and it’s SAM surveys.Facultad de Informática2021info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf33-36http://sedici.unlp.edu.ar/handle/10915/125145enginfo:eu-repo/semantics/altIdentifier/isbn/978-950-34-2016-4info:eu-repo/semantics/reference/hdl/10915/121564info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T17:11:04Zoai:sedici.unlp.edu.ar:10915/125145Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 17:11:04.54SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Speech emotion representation : A method to convert discrete to dimensional emotional models for emotional inference multimodal frameworks
title Speech emotion representation : A method to convert discrete to dimensional emotional models for emotional inference multimodal frameworks
spellingShingle Speech emotion representation : A method to convert discrete to dimensional emotional models for emotional inference multimodal frameworks
Elkfury, Fernando
Ciencias Informáticas
Emotions
Multimodal Framework
Affective computing
title_short Speech emotion representation : A method to convert discrete to dimensional emotional models for emotional inference multimodal frameworks
title_full Speech emotion representation : A method to convert discrete to dimensional emotional models for emotional inference multimodal frameworks
title_fullStr Speech emotion representation : A method to convert discrete to dimensional emotional models for emotional inference multimodal frameworks
title_full_unstemmed Speech emotion representation : A method to convert discrete to dimensional emotional models for emotional inference multimodal frameworks
title_sort Speech emotion representation : A method to convert discrete to dimensional emotional models for emotional inference multimodal frameworks
dc.creator.none.fl_str_mv Elkfury, Fernando
Ierache, Jorge Salvador
author Elkfury, Fernando
author_facet Elkfury, Fernando
Ierache, Jorge Salvador
author_role author
author2 Ierache, Jorge Salvador
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Emotions
Multimodal Framework
Affective computing
topic Ciencias Informáticas
Emotions
Multimodal Framework
Affective computing
dc.description.none.fl_txt_mv Computer-Human interaction is more frequent now than ever before, thus the main goal of this research area is to improve communication with computers, so it becomes as natural as possible. A key aspect to achieve such interaction is the affective component often missing from last decade developments. To improve computer human interaction in this paper we present a method to convert discrete or categorical data from a CNN emotion classifier trained with Mel scale spectrograms to a two-dimensional model, pursuing integration of the human voice as a feature for emotional inference multimodal frameworks. Lastly, we discuss preliminary results obtained from presenting audiovisual stimuli to different subject and comparing dimensional arousal-valence results and it’s SAM surveys.
Facultad de Informática
description Computer-Human interaction is more frequent now than ever before, thus the main goal of this research area is to improve communication with computers, so it becomes as natural as possible. A key aspect to achieve such interaction is the affective component often missing from last decade developments. To improve computer human interaction in this paper we present a method to convert discrete or categorical data from a CNN emotion classifier trained with Mel scale spectrograms to a two-dimensional model, pursuing integration of the human voice as a feature for emotional inference multimodal frameworks. Lastly, we discuss preliminary results obtained from presenting audiovisual stimuli to different subject and comparing dimensional arousal-valence results and it’s SAM surveys.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/125145
url http://sedici.unlp.edu.ar/handle/10915/125145
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-950-34-2016-4
info:eu-repo/semantics/reference/hdl/10915/121564
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
33-36
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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