Speech Features for Discriminating Stress Using Branch and Bound Wrapper Search
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10451/31084 |
Resumo: | Stress detection from speech is a less explored field than Automatic Emotion Recognition and it is still not clear which features are better stress discriminants. VOCE aims at doing speech classification as stressed or not-stressed in real-time, using acoustic-prosodic features only. We therefore look for the best discriminating feature subsets from a set of 6285 features – 6125 features extracted with openSMILE toolkit and 160 Teager Energy Operator (TEO) features. We use a mutual information filter and a branch and bound wrapper heuristic with an SVM classifier to perform feature selection. Since many feature sets are selected, we analyse them in terms of chosen features and classifier performance concerning also true positive and false positive rates. The results show that the best feature types for our application case are Audio Spectral, MFCC, PCM and TEO. We reached results as high as 70.36% for generalisation accuracy |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Speech Features for Discriminating Stress Using Branch and Bound Wrapper SearchStressEmotion recognitionEcological dataFeature setsFeature selectionStress detection from speech is a less explored field than Automatic Emotion Recognition and it is still not clear which features are better stress discriminants. VOCE aims at doing speech classification as stressed or not-stressed in real-time, using acoustic-prosodic features only. We therefore look for the best discriminating feature subsets from a set of 6285 features – 6125 features extracted with openSMILE toolkit and 160 Teager Energy Operator (TEO) features. We use a mutual information filter and a branch and bound wrapper heuristic with an SVM classifier to perform feature selection. Since many feature sets are selected, we analyse them in terms of chosen features and classifier performance concerning also true positive and false positive rates. The results show that the best feature types for our application case are Audio Spectral, MFCC, PCM and TEO. We reached results as high as 70.36% for generalisation accuracySpringerRepositório da Universidade de LisboaJulião, MarianaSilva, JorgeAguiar, AnaMoniz, HelenaBatista, Fernando2018-01-28T15:10:59Z20152015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/31084engJulião, M., Silva, J., Aguiar, A., Moniz, H. & Batista, F. (2015) Speech features for discriminating stress using branch and bound wrapper search, InSLATE'15, Springer, series CCIS, Madrid, Spain.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T16:24:15Zoai:repositorio.ul.pt:10451/31084Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:46:36.620417Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Speech Features for Discriminating Stress Using Branch and Bound Wrapper Search |
title |
Speech Features for Discriminating Stress Using Branch and Bound Wrapper Search |
spellingShingle |
Speech Features for Discriminating Stress Using Branch and Bound Wrapper Search Julião, Mariana Stress Emotion recognition Ecological data Feature sets Feature selection |
title_short |
Speech Features for Discriminating Stress Using Branch and Bound Wrapper Search |
title_full |
Speech Features for Discriminating Stress Using Branch and Bound Wrapper Search |
title_fullStr |
Speech Features for Discriminating Stress Using Branch and Bound Wrapper Search |
title_full_unstemmed |
Speech Features for Discriminating Stress Using Branch and Bound Wrapper Search |
title_sort |
Speech Features for Discriminating Stress Using Branch and Bound Wrapper Search |
author |
Julião, Mariana |
author_facet |
Julião, Mariana Silva, Jorge Aguiar, Ana Moniz, Helena Batista, Fernando |
author_role |
author |
author2 |
Silva, Jorge Aguiar, Ana Moniz, Helena Batista, Fernando |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Julião, Mariana Silva, Jorge Aguiar, Ana Moniz, Helena Batista, Fernando |
dc.subject.por.fl_str_mv |
Stress Emotion recognition Ecological data Feature sets Feature selection |
topic |
Stress Emotion recognition Ecological data Feature sets Feature selection |
description |
Stress detection from speech is a less explored field than Automatic Emotion Recognition and it is still not clear which features are better stress discriminants. VOCE aims at doing speech classification as stressed or not-stressed in real-time, using acoustic-prosodic features only. We therefore look for the best discriminating feature subsets from a set of 6285 features – 6125 features extracted with openSMILE toolkit and 160 Teager Energy Operator (TEO) features. We use a mutual information filter and a branch and bound wrapper heuristic with an SVM classifier to perform feature selection. Since many feature sets are selected, we analyse them in terms of chosen features and classifier performance concerning also true positive and false positive rates. The results show that the best feature types for our application case are Audio Spectral, MFCC, PCM and TEO. We reached results as high as 70.36% for generalisation accuracy |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015 2015-01-01T00:00:00Z 2018-01-28T15:10:59Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10451/31084 |
url |
http://hdl.handle.net/10451/31084 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Julião, M., Silva, J., Aguiar, A., Moniz, H. & Batista, F. (2015) Speech features for discriminating stress using branch and bound wrapper search, InSLATE'15, Springer, series CCIS, Madrid, Spain. |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799134391161585664 |