AUTOMATIC EMOTION RECOGNITION IN SPEECH SIGNAL USING TEAGER ENERGY OPERATOR AND MFCC FEATURES
A new approach to feature extraction for the automatic emotion classification in speech signals was described and tested in this work.The method was based on the Teager energy operator combined with mel-frequency cepstral coefficients (TEO-MFCC).The proposed TEO-MFCC method was tested using speech recordings collected from the Speech Under Simulated and Actual Stress (SUSAS) database with three simulated emotions (angry, neutral and soft).The Gaussian mixture model (GMM) was used as classifier. The average classification accuracy for three emotions reached up to 73％, much higher than purely guess (33％ for three emotions).Especially, the system showed good performance on the classification of emotion angry.The correct recognition rate for emotion angry was 83％, while it was only 59％ for emotion neutral and 77％ for emotion soft.
Automatic emotion recognition Teager energy operator Mel-frequency cesptral coefficient Gaussian mixture models
LING HE MARGARET LECH NICHOLAS ALLEN
Department of Medical Informatics and Engineering,Sichuan University,China School of Electrical and Computer Engineering,RMIT University,Australia Faculty of Medicine,Dentistry and Health Sciences,The University of Melbourne,Australia
国际会议
成都
英文
2313-2317
2011-11-25（万方平台首次上网日期，不代表论文的发表时间）