Hdl Handle:
http://hdl.handle.net/10149/96035
Title:
Affect recognition from speech
Book Title:
Artificial intelligence in education - Building learning systems that care: From knowledge representation to affective modelling
Authors:
Zhang, L. (Li); Francisco, V. (Virginia)
Editors:
Dimitrova, V. (Vania); Mizoguchi, R. (Riichiro); du Boulay, B. (Benedict); Graesser, A. (Art)
Affiliation:
University of Teesside. School of Computing.
Citation:
Zhang, L. and Francisco, V. (2009) 'Affect recognition from speech', Artificial intelligence in education - Building learning systems that care: From knowledge representation to affective modelling, Frontiers in artificial intelligence and applications, 200 (1), pp.683-685.
Publisher:
IOS Press
Issue Date:
Jun-2009
URI:
http://hdl.handle.net/10149/96035
DOI:
10.3233/978-1-60750-028-5-683
Abstract:
We aim to provide an automatic anti-bullying component in online text and speech based interaction for young people age 18 - 25. Affect expression in speech generally differs from culture to culture, from female to male. In this study, we focus on affect sensing from speech for different gender user groups for young people. So far our work mainly concentrates on the sensing of five basic emotions (including 'happiness', 'sadness', 'fear', 'surprise', and 'anger') and 'neutral' from speech. Detailed acoustic features have been extracted after analysis of speech data from one chosen male and female speaker. Our affect sensing component has been implemented under the theory of naïve Bayes classifier. We have also evaluated it using new test data. Our work contributes to the conference themes on intelligent technologies - machine learning and affective speech processing.
Type:
Meetings and Proceedings; Book Chapter
Language:
en
Keywords:
affect sensing; naïve Bayes classifier; speech processing; anti-bullying
Series/Report no.:
Frontiers in artificial intelligence and applications
ISBN:
9781607500285
Rights:
Subject to restrictions, author can archive publisher's version/PDF. For full details see http://www.sherpa.ac.uk/romeo/ [Accessed 09/04/2010]
Citation Count:
0 [Scopus, 08/04/2010]

Full metadata record

DC FieldValue Language
dc.contributor.authorZhang, L. (Li)en
dc.contributor.authorFrancisco, V. (Virginia)-
dc.contributor.editorDimitrova, V. (Vania)-
dc.contributor.editorMizoguchi, R. (Riichiro)-
dc.contributor.editordu Boulay, B. (Benedict)-
dc.contributor.editorGraesser, A. (Art)-
dc.date.accessioned2010-04-08T15:00:05Z-
dc.date.available2010-04-08T15:00:05Z-
dc.date.issued2009-06-
dc.identifier.isbn9781607500285-
dc.identifier.doi10.3233/978-1-60750-028-5-683-
dc.identifier.urihttp://hdl.handle.net/10149/96035-
dc.description.abstractWe aim to provide an automatic anti-bullying component in online text and speech based interaction for young people age 18 - 25. Affect expression in speech generally differs from culture to culture, from female to male. In this study, we focus on affect sensing from speech for different gender user groups for young people. So far our work mainly concentrates on the sensing of five basic emotions (including 'happiness', 'sadness', 'fear', 'surprise', and 'anger') and 'neutral' from speech. Detailed acoustic features have been extracted after analysis of speech data from one chosen male and female speaker. Our affect sensing component has been implemented under the theory of naïve Bayes classifier. We have also evaluated it using new test data. Our work contributes to the conference themes on intelligent technologies - machine learning and affective speech processing.en
dc.language.isoenen
dc.publisherIOS Pressen
dc.relation.ispartofseriesFrontiers in artificial intelligence and applications-
dc.rightsSubject to restrictions, author can archive publisher's version/PDF. For full details see http://www.sherpa.ac.uk/romeo/ [Accessed 09/04/2010]-
dc.subjectaffect sensingen
dc.subjectnaïve Bayes classifieren
dc.subjectspeech processingen
dc.subjectanti-bullyingen
dc.titleAffect recognition from speechen
dc.typeMeetings and Proceedings-
dc.typeBook Chapteren
dc.contributor.departmentUniversity of Teesside. School of Computing.en
dc.title.bookArtificial intelligence in education - Building learning systems that care: From knowledge representation to affective modelling-
ref.citationcount0 [Scopus, 08/04/2010]en
or.citation.harvardZhang, L. and Francisco, V. (2009) 'Affect recognition from speech', Artificial intelligence in education - Building learning systems that care: From knowledge representation to affective modelling, Frontiers in artificial intelligence and applications, 200 (1), pp.683-685.-
prism.startingPage683-
prism.endingPage685-
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