Speech Emotion Recognition Using Semi-Definite Programming Multiple-Kernel SVM

doi:10.13190/j.jbupt.2015.s1.016

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2015, Vol. 38 ›› Issue (s1): 67-71.doi: 10.13190/j.jbupt.2015.s1.016

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Speech Emotion Recognition Using Semi-Definite Programming Multiple-Kernel SVM

JIANG Xiao-qing^1,2, XIA Ke-wen¹, XIA Xin-yuan¹, ZU Bao-kai¹

1. School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China;
2. School of Information Science and Engineering, University of Jinan, Jinan 250022, China

Received:2014-07-08 Online:2015-06-28 Published:2015-06-28

Abstract

Abstract:

To improve the accuracy of speech emotion recognition, a multi-class classifier with binary-tree structure is adopted, which includes building the multi-kernel support vector machine (SVM) classifier model solved by semi-definite programming method, and using the root mean square error and maximum error to evaluate the performance of the classifier. Through the test on the parameter set obtained by feature selection algorithm, the results of experiments show that the total recognition accuracy of the proposed multiple-kernel SVM classifier model using semi-definite programming is 88.614%, which is 12.376% higher than that of single-kernel SVM model. Moreover the multiple-kernel SVM model can reduce the total error accumulation and confusion between emotion states.

Key words: speech emotion recognition, multiple-kernel support vector machine, semi-definite programming

CLC Number:

TP18

JIANG Xiao-qing, XIA Ke-wen, XIA Xin-yuan, ZU Bao-kai. Speech Emotion Recognition Using Semi-Definite Programming Multiple-Kernel SVM[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2015, 38(s1): 67-71.

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Speech Emotion Recognition Using Semi-Definite Programming Multiple-Kernel SVM

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