基于改进型SVM算法的语音情感识别  

Speech emotion recognition algorithm based on modified SVM

作　　者：李书玲[1] 刘蓉[1] 张鎏钦[1] 刘红[1] 

机构地区：[1]华中师范大学物理科学与技术学院,武汉430079

出　　处：《计算机应用》2013年第7期1938-1941,共4页journal of Computer Applications

基　　金：国家社会科学基金资助项目(12BTQ038);国家自然科学基金资助项目(61202470)

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。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.

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 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]