Abstract: In order to effectively improve the recognition accuracy of the speech emotion recognition system, an improved speech emotion recognition algorithm based on Support Vector Machine (SVM) was proposed. In the proposed algorithm, the SVM parameters, penalty factor and nuclear function parameter, were optimized with genetic algorithm. Furthermore, an emotion recognition model was established with SVM method. The performance of this algorithm was assessed by computer simulations, and 91.03% and 96.59% recognition rates were achieved respectively in seven-emotion recognition experiments and common five-emotion recognition experiments on the Berlin database. When the Chinese emotional database was used, the rate increased to 97.67%. The obtained results of the simulations demonstrate the validity of the proposed algorithm.

Key words: Support Vector Machine (SVM), speech emotion recognition, speech signal, parameter optimization, Genetic Algorithm （GA）

摘要： 为有效提高语音情感识别系统的识别率，研究分析了一种改进型的支持向量机(SVM)算法。该算法首先利用遗传算法对SVM参数惩罚因子和核函数中参数进行优化，然后用优化后的参数进行语音情感的建模与识别。在柏林数据集上进行7种和常用5种情感识别实验，取得了91.03%和96.59%的识别率，在汉语情感数据集上，取得了97.67%的识别率。实验结果表明该算法能够有效识别语音情感。

关键词: 支持向量机, 语音情感识别, 语音信号, 参数优化, 遗传算法