Abstract: This paper proposes a method of speech emotion recognition based on fusion of sample entropy and Mel-frequency Cepstral Coefficients(MFCC). Sample entropy statistic and MFCC are modeled with Support Vector Machine(SVM) respectively to obtain the probabilities of happy, angry, bored and afraid. The sum and product rules are used to fuse the probabilities to gain the final decision. Simulation results demonstrate that the recognition rate obtained with the proposed method is high.

Key words: speech emotion recognition, sample entropy, Mel-frequency Cepstral Coefficients(MFCC), Support Vector Machine(SVM)

摘要： 提出一种基于样本熵与Mel频率倒谱系数(MFCC)融合的语音情感识别方法。利用支持向量机分别对样本熵统计量与MFCC进行处理，计算其属于高兴、生气、厌烦和恐惧4种情感的概率，采用加法规则和乘法规则对情感概率进行融合，得到识别结果。仿真实验结果表明，该方法的识别率较高。

关键词: 语音情感识别, 样本熵, Mel频率倒谱系数, 支持向量机

CLC Number: 

• TP391.42