Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/71839
Title: Speech based emotion classification
Authors: Nwe, T.L. 
Say, F.W. 
De Silva, L.C. 
Keywords: Emotions of speech
Hidden Markov Model
Mel-frequency speech power coefficients
Speech recognition
Issue Date: 2001
Citation: Nwe, T.L.,Say, F.W.,De Silva, L.C. (2001). Speech based emotion classification. IEEE Region 10 International Conference on Electrical and Electronic Technology : 297-301. ScholarBank@NUS Repository.
Abstract: In this paper, a speech based emotion classification method is presented. Six basic human emotions including anger, dislike, fear, happiness, sadness and surprise are investigated. The recognizer presented in this paper is based on the Discrete Hidden Markov Model and a novel feature vector based on Mel frequency short time speech power coefficients is proposed. A universal codebook is constructed based on emotions under observation for each experiment. The databases consist of 90 emotional utterances each from two speakers. Several experiments including ungrouped emotion classification and grouped emotion classification are conducted. For the ungrouped emotion classification, an average accuracy of 72.22% and 60% are obtained respectively for utterances of the two speakers. For grouped emotion classification, higher accuracy of 94.44% and 70% are achieved.
Source Title: IEEE Region 10 International Conference on Electrical and Electronic Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/71839
ISBN: 0780371011
Appears in Collections:Staff Publications

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