HMM-Based Speaker Emotional Recognition Technology for Speech Signal

摘要：

　　In emotion classification of speech signals,the popular features employed are statistics of fundamental frequency,energy contour,duration of silence and voice quality.However,the performance of systems employing these features degrades substantially when more than two categories of emotion are to be classified.In this paper,a text independent method of emotion classification of speech is proposed.The proposed method makes use of short time log frequency power coefficients(LFPC) to represent the speech signals and a discrete Hidden Markov Model (HMM) as the classifier.The category labels used are,the archetypal emotions of anger,joy,sadness and neutral.Results show that the proposed system yields an average accuracy of 82.55%and the best accuracy of 94.4% in the classification of 4 emotions.Results also reveal that LFPC is a better choice as feature parameters for emotion classification than the traditional feature parameters.

关键词： Speech emotional recognition Hidden Markov Model (HMM) Log frequency power coefficients(LFPC) Emotional feature vector

作者: Yuqiang Qin Xueying Zhang

作者单位: Taiyuan University of Technology, Taiyuan ,China ;Taiyuan University of Science and Technology, Taiy Taiyuan University of Technology, Taiyuan ,China

会议类型: 国际会议

会议名称: the 2011 International Conference on Frontiers of Manufacturing Science and Measuring Technology (第一届制造科学与检测技术国际会议(ICFMM2011))

会议地点: 重庆

会议语种:英文

页码: 261-265

在线出版日期: 2011-06-23（万方平台首次上网日期，不代表论文的发表时间）