Volume 7 Number 12 (Dec. 2012)
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JSW 2012 Vol.7(12): 2726-2733 ISSN: 1796-217X
doi: 10.4304//jsw.7.12.2726-2733

Speech Emotion Recognition based on Optimized Support Vector Machine

Bo Yu1, 2, Haifeng Li1 and Chunying Fang1

1School of Computer Science and Technology/Harbin Institute of Technology, Harbin, China
2Software College/Harbin University of Science and Technology, Harbin, China

Abstract—Speech emotion recognition is a very important speech technology. In this paper, Mel Frequency Cepstral Coefficients (MFCC) has been used to represent speech signal as emotional features. MFCCs plus energy of an utterance are used as the input for Support Vector Machine. Support Vector Machine (SVM) has been profoundly successful in the area of pattern recognition. In the recent years there has been use of SVM for speech recognition. Many kinds of kernel functions are available for SVM to map an input space problem to high dimensional spaces. We lack guidelines on choosing a better kernel with optimized parameters of SVM. Some kernels are better for some questions, but worse for other questions. Which is better is unknown for speech emotion recognition, thus the thesis studies the SVM classifier and proposes methods used to select a better kernel with optimized parameters. The new method we proposed in this paper can more efficiently gain optimized parameters than common methods. In order to improve recognition accuracy rate of the speech emotion recognition system, a speech emotion recognition based on optimized support vector machine is proposed. Experimental studies are performed over the HIT Emotional Speech Database established by Speech Processing Lab in School of Computer Science and Technology at HIT. The experiment result shows that the speech emotion recognition based on optimized SVM can improve the performance of the emotion recognition system effectively

Index Terms—speech emotion recognition, MFCC optimized SVM, kernel function


Cite: Bo Yu, Haifeng Li and Chunying Fang "Speech Emotion Recognition based on Optimized Support Vector Machine," Journal of Software vol. 7, no. 12, pp. 2726-2733, 2012.

General Information

ISSN: 1796-217X (Online)
Frequency:  Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKIGoogle Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsweditorialoffice@gmail.com
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