An Optimization Method for Support Vector Machine Applied to Speech Emotion Recognition
1 Department of Electrical Information and Engineering, Changchun University, China
2 Department of Information Engineering, Changchun University of Finance and Economics, China
a Corresponding author: firstname.lastname@example.org
On the basis of the analysis of support vector machine model, an improved MFCC feature parameters have been adopted. Support vector machine has been used as identification model of speech emotion recognition system. For classification problems of support vector machine, an algorithm of optimization parameters has been presented. The algorithm improves the speed of solving and accuracy, and has a good classification results. Experimental results show that, in different environment, the parameter optimization method increases the recognition rate, reduces training time and has good robustness compared to traditional methods.
© Owned by the authors, published by EDP Sciences, 2015
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