关键词: 语音情感识别, Mel频率倒谱系数, 经验模态分解, 支持向量机

Abstract: Non-stationary characteristics of speech signal under the different emotions are especially obvious. Traditional MFCC can only reflect speech static features, while EMD can describe non-stationary characteristics of speech signal precisely. In order to extract the non-stationary features of emotional speech, the improved MFCC steps are proposed including EMD decomposition into IMFs, Mel filtering, logarithm and DCT. The improved MFCC is adopted as the new feature with SVM to recognize four speech emotions consisting of happy, angry, bored and fear. Simulation results demonstrate that the recognition rate of the improved MFCC is 77.17%, and in different SNRs, the recognition rate can be increased by 3.26%.

Key words: speech emotion recognition, Mel-Frequency Cepstral Coefficients（MFCC）, Empirical Mode Decomposition（EMD）, Support Vector Machine（SVM）