采用半定规划多核SVM的语音情感识别  

Speech Emotion Recognition Using Semi-Definite Programming Multiple-Kernel SVM

作　　者：姜晓庆[1,2] 夏克文[1] 夏莘媛 祖宝开 

机构地区：[1]河北工业大学电子信息工程学院,天津300401 [2]济南大学信息科学与工程学院,济南250022

出　　处：《北京邮电大学学报》2015年第B06期67-71,共5页Journal of Beijing University of Posts and Telecommunications

基　　金：国家自然科学基金项目(51208168);天津市自然科学基金项目(11JCYBJC00900;13JCYBJC37700);河北省自然科学基金项目(F2013202254;F2013202102);河北省引进留学人员基金项目(C2012003038);济南大学科研基金项目(XKY1317)

摘　　要：为提高语音情感识别精度,采用二叉树结构设计多分类器,其中使用半定规划法求解并构造多核支持向量机(SVM)分类模型,并采用均方根误差与最大误差对分类器性能进行衡量.对特征选择之后的参数集合进行了测试,结果表明,采用半定规划多核SVM分类模型的情感识别精度达到88.614%,比单核分类模型的识别精度提高了12.376%,且能有效减少误差积累和降低情感状态之间混淆程度.To improve the accuracy of speech emotion recognition, a multi-class classifier with binary- tree structure is adopted, which includes building the multi-kernel support vector machine （SVM） classi- fier model solved by semi-definite programming method, and using the root mean square error and maxi- mum error to evaluate the performance of the classifier. Through the test on the parameter set obtained by feature selection algorithm, the results of experiments show that the total recognition accuracy of the pro- posed multiple-kernel SVM classifier model using semi-definite programming is 88. 614%, which is 12. 376% higher than that of single-kernel SVM model. Moreover the multiple-kernel SVM model can re- duce the total error accumulation and confusion between emotion states.

关 键 词：语音情感识别 多核支持向量机 半定规划 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]