Speech emotion recognition based on a hybrid of HMM/ANN

Authors:
Xia Mao;Bing Zhang;Yi Luo
Affiliations:
School of Electronic and Information Engineering, Beihang University, Beijing, China;School of Electronic and Information Engineering, Beihang University, Beijing, China;School of Electronic and Information Engineering, Beihang University, Beijing, China
Venue:
AIC'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Informatics and Communications - Volume 7
Year:
2007

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Abstract

Speech emotion recognition, as a vital part of affective human computer interaction, has become a new challenge to speech processing. In this paper, a hybrid of hidden Markov models (HMMs) and artificial neural network (ANN) has been proposed to classify emotions, combining advantage on capability to dynamic time warping of HMM and pattern recognition of ANN. HMMs, which export likelihood probabilities and optimal state sequences, have been used to model speech feature sequences, while ANN has been employed to make a decision. The recognition result of the hybrid classification has been compared with the isolated HMMs by two speech corpora, Germany database and Mandarin database, and the average recognition rates have reached 83.8% and 81.6% respectively.