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
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/125145
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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:UNLP2025-09-29T11:30:08Zoai: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:13292025-09-29 11:30:08.446SEDICI (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. |
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2021 |
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2021 |
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