Speech based emotion classification

Author

Nwe, Tin Lay ; Wei, Foo Say ; De Silva, Liyanage C.

Author_Institution

Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore

Volume

1

fYear

2001

fDate

2001

Firstpage

297

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

Keywords

hidden Markov models; psychology; speech recognition; anger; discrete hidden Markov model; dislike; emotional utterances; fear; feature vector; grouped emotion classification; happiness; human emotions; mel frequency short time speech power coefficients; sadness; speech based emotion classification; surprise; ungrouped emotion classification; universal codebook; Data mining; Education; Emotion recognition; Face detection; Frequency; Hidden Markov models; Humans; Loudspeakers; Neural networks; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON 2001. Proceedings of IEEE Region 10 International Conference on Electrical and Electronic Technology

Print_ISBN

0-7803-7101-1

Type

conf

DOI

10.1109/TENCON.2001.949600

Filename

949600