Speech emotion recognition using hidden Markov models
Document typeConference lecture
Rights accessRestricted access - publisher's policy
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.
CitationNogueiras, A. [et al.]. Speech emotion recognition using hidden Markov models. A: Eurospeech 2001. "Eurospeech 2001 : European Conference on Speech Communication and Technology : proceedings & book of abstracts : cd-rom proceedings". Aalborg: 2001, p. 1-4.
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