Speech emotion recognition using hidden Markov models

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Title:	Speech emotion recognition using hidden Markov models
Author:	Nogueiras Rodríguez, Albino; Mariño Acebal, José Bernardo; Bonafonte Cávez, Antonio; Moreno Bilbao, M. Asunción
Other authors:	Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
Abstract:	This paper introduces a first approach to emotion recognition using RAMSES, the UPC’s speech recognition system. The approach is based on standard speech recognition technology using hidden semi-continuous Markov models. Both the selection of low level features and the design of the recognition system are addressed. Results are given on speaker dependent emotion recognition using the Spanish corpus of INTERFACE Emotional Speech Synthesis Database. The accuracy recognising seven different emotions—the six ones defined in MPEG-4 plus neutral style—exceeds 80% using the best combination of low level features and HMM structure. This result is very similar to that obtained with the same database in subjective evaluation by human judges.
Abstract:	Peer Reviewed
Subject(s):	-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic -Automatic speech recognition -Processament de la parla
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Document type:	Article - Published version Conference Object
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