A Study of Speech Emotion Recognition Based on Hybrid Algorithm

摘要：

To effectively improve the recognition accuracy of the speech emotion recognition system, a hybrid algorithm which combines Continuous Hidden Markov Model (CHMM), All-Class-in-One Neural Network (ACON) and Support Vector Machine (SVM) is proposed. In SVM and ACON methods, some global statistics are used as emotional features, while in CHMM method, instantaneous features are employed. The recognition rate by the proposed method is 92.25％ , with the rejection rate to be 0.78％. Furthermore, it obtains the relative increasing of 8.53％, 4.69％ and 0.78％ compared with ACON, CHMM and SVM methods respectively. The experiment result confirms the efficiency of distinguishing anger, happiness, neutral and sadness emotional states.

关键词： emotion recognition speech signal emotional features hybrid algorithm Support Vector Machine(SVM) All-Class-in-One Neural Network (ACON) continuous hidden Markov model (CHMM)

作者: Zhu Ju-xia Zhang Chao Lv Zhao Rao Yao-quan Wu Xiao-pei

作者单位: School of Computer Science and Technology Anhui University Hefei, China

会议类型: 国际会议

会议名称: 2010 International Conference on Signal and Information Processing(2010年IEEE信号与信息处理国际会议 ICSIP2010)

会议地点: 长沙

会议语种:英文

页码: 113-117

在线出版日期: 2010-12-14（万方平台首次上网日期，不代表论文的发表时间）