Emotion Recognition in Never-Seen Languages Using a Novel Ensemble Method With Emotion Profiles

Autores
Albornoz, Enrique Marcelo; Milone, Diego Humberto
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Over the last years, researchers have addressed emotional state identification because it is an important issue to achieve more natural speech interactive systems. There are several theories that explain emotional expressiveness as a result of natural evolution, as a social construction, or a combination of both. In this work, we propose a novel system to model each language independently, preserving the cultural properties. In a second stage, we use the concept of universality of emotions to map and predict emotions in never-seen languages. Features and classifiers widely tested for similar tasks were used to set the baselines. We developed a novel ensemble classifier to deal with multiple languages and tested it on never-seen languages. Furthermore, this ensemble uses the Emotion Profiles technique in order to map features from diverse languages in a more tractable space. The experiments were performed in a language-independent scheme. Results show that the proposed model improves the baseline accuracy, whereas its modular design allows the incorporation of a new language without having to train the whole system.
Fil: Albornoz, Enrique Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Materia
Emotion Recognition
Ensemble Classifier
Emotion Profiles
Not-Yet-Encountered Languages
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/47047
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Emotion Recognition in Never-Seen Languages Using a Novel Ensemble Method With Emotion ProfilesAlbornoz, Enrique MarceloMilone, Diego HumbertoEmotion RecognitionEnsemble ClassifierEmotion ProfilesNot-Yet-Encountered Languageshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Over the last years, researchers have addressed emotional state identification because it is an important issue to achieve more natural speech interactive systems. There are several theories that explain emotional expressiveness as a result of natural evolution, as a social construction, or a combination of both. In this work, we propose a novel system to model each language independently, preserving the cultural properties. In a second stage, we use the concept of universality of emotions to map and predict emotions in never-seen languages. Features and classifiers widely tested for similar tasks were used to set the baselines. We developed a novel ensemble classifier to deal with multiple languages and tested it on never-seen languages. Furthermore, this ensemble uses the Emotion Profiles technique in order to map features from diverse languages in a more tractable space. The experiments were performed in a language-independent scheme. Results show that the proposed model improves the baseline accuracy, whereas its modular design allows the incorporation of a new language without having to train the whole system.Fil: Albornoz, Enrique Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; ArgentinaIEEE2017-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/47047Albornoz, Enrique Marcelo; Milone, Diego Humberto; Emotion Recognition in Never-Seen Languages Using a Novel Ensemble Method With Emotion Profiles; IEEE; IEEE Transactions on Affective Computing; 8; 1-2017; 43-531949-3045CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.computer.org/csdl/trans/ta/preprint/07337399.pdfinfo:eu-repo/semantics/altIdentifier/doi/10.1109/TAFFC.2015.2503757info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:41:39Zoai:ri.conicet.gov.ar:11336/47047instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:41:39.774CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Emotion Recognition in Never-Seen Languages Using a Novel Ensemble Method With Emotion Profiles
title Emotion Recognition in Never-Seen Languages Using a Novel Ensemble Method With Emotion Profiles
spellingShingle Emotion Recognition in Never-Seen Languages Using a Novel Ensemble Method With Emotion Profiles
Albornoz, Enrique Marcelo
Emotion Recognition
Ensemble Classifier
Emotion Profiles
Not-Yet-Encountered Languages
title_short Emotion Recognition in Never-Seen Languages Using a Novel Ensemble Method With Emotion Profiles
title_full Emotion Recognition in Never-Seen Languages Using a Novel Ensemble Method With Emotion Profiles
title_fullStr Emotion Recognition in Never-Seen Languages Using a Novel Ensemble Method With Emotion Profiles
title_full_unstemmed Emotion Recognition in Never-Seen Languages Using a Novel Ensemble Method With Emotion Profiles
title_sort Emotion Recognition in Never-Seen Languages Using a Novel Ensemble Method With Emotion Profiles
dc.creator.none.fl_str_mv Albornoz, Enrique Marcelo
Milone, Diego Humberto
author Albornoz, Enrique Marcelo
author_facet Albornoz, Enrique Marcelo
Milone, Diego Humberto
author_role author
author2 Milone, Diego Humberto
author2_role author
dc.subject.none.fl_str_mv Emotion Recognition
Ensemble Classifier
Emotion Profiles
Not-Yet-Encountered Languages
topic Emotion Recognition
Ensemble Classifier
Emotion Profiles
Not-Yet-Encountered Languages
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Over the last years, researchers have addressed emotional state identification because it is an important issue to achieve more natural speech interactive systems. There are several theories that explain emotional expressiveness as a result of natural evolution, as a social construction, or a combination of both. In this work, we propose a novel system to model each language independently, preserving the cultural properties. In a second stage, we use the concept of universality of emotions to map and predict emotions in never-seen languages. Features and classifiers widely tested for similar tasks were used to set the baselines. We developed a novel ensemble classifier to deal with multiple languages and tested it on never-seen languages. Furthermore, this ensemble uses the Emotion Profiles technique in order to map features from diverse languages in a more tractable space. The experiments were performed in a language-independent scheme. Results show that the proposed model improves the baseline accuracy, whereas its modular design allows the incorporation of a new language without having to train the whole system.
Fil: Albornoz, Enrique Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
description Over the last years, researchers have addressed emotional state identification because it is an important issue to achieve more natural speech interactive systems. There are several theories that explain emotional expressiveness as a result of natural evolution, as a social construction, or a combination of both. In this work, we propose a novel system to model each language independently, preserving the cultural properties. In a second stage, we use the concept of universality of emotions to map and predict emotions in never-seen languages. Features and classifiers widely tested for similar tasks were used to set the baselines. We developed a novel ensemble classifier to deal with multiple languages and tested it on never-seen languages. Furthermore, this ensemble uses the Emotion Profiles technique in order to map features from diverse languages in a more tractable space. The experiments were performed in a language-independent scheme. Results show that the proposed model improves the baseline accuracy, whereas its modular design allows the incorporation of a new language without having to train the whole system.
publishDate 2017
dc.date.none.fl_str_mv 2017-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/47047
Albornoz, Enrique Marcelo; Milone, Diego Humberto; Emotion Recognition in Never-Seen Languages Using a Novel Ensemble Method With Emotion Profiles; IEEE; IEEE Transactions on Affective Computing; 8; 1-2017; 43-53
1949-3045
CONICET Digital
CONICET
url http://hdl.handle.net/11336/47047
identifier_str_mv Albornoz, Enrique Marcelo; Milone, Diego Humberto; Emotion Recognition in Never-Seen Languages Using a Novel Ensemble Method With Emotion Profiles; IEEE; IEEE Transactions on Affective Computing; 8; 1-2017; 43-53
1949-3045
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.computer.org/csdl/trans/ta/preprint/07337399.pdf
info:eu-repo/semantics/altIdentifier/doi/10.1109/TAFFC.2015.2503757
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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