Speech Emotion Recognition based on Optimized Support Vector Machine

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.