Features importance analysis for emotional speech classification

Authors:
Jianhua Tao;Yongguo Kang
Affiliations:
National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing;National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, Beijing
Venue:
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Year:
2005

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Abstract

The paper analyzes the prosody features, which includes the intonation, speaking rate, intensity, based on classified emotional speech. As an important feature of voice quality, voice source are also deduced for analysis. With the analysis results above, the paper creates both a CART model and a weight decay neural network model to find acoustic importance towards the emotional speech classification and to disclose whether there is an underlying consistency between acoustic features and speech emotion. The result shows the proposed method can obtain the importance of each acoustic feature through its weight for emotional speech classification and further improve the emotional speech classification.