Feature Learning via Deep Belief Network for Chinese Speech Emotion Recognition

摘要：

　　Speech emotion recognition is an interesting and challenging subject due to the emotion gap between speech signals and high-level speech emotion.To bridge this gap,this paper present a method of Chinese speech emotion recognition using Deep belief networks(DBN).DBN is used to perform unsupervised feature learning on the extracted low-level acoustic features.Then,Multi-layer Perceptron(MLP)is initialized in terms of the learning results of hidden layer of DBN,and employed for Chinese speech emotion classification.Experimental results on the Chinese Natural Audio-Visual Emotion Database(CHEAVD),show that the presented method obtains a classification accuracy of 32.80%and macro average precision of 41.54%on the testing data from the CHEAVD dataset on speech emotion recognition tasks,significantly outperforming the baseline results provided by the organizers in the speech emotion recognition sub-challenges.

关键词： Deep learning Deep belief networks Speech emotion recognition Feature learning

作者: Shiqing Zhang Xiaoming Zhao Yuelong Chuang Wenping Guo Ying Chen

作者单位: Institute of Intelligent Information Processing,Taizhou University,Taizhou,China

会议类型: 国际会议

会议名称: 第七届全国模式识别学术会议(The 7th Chinese Conference on Pattern Recognition,CCPR2016)

会议地点: 成都

会议语种:英文

页码: 645-651

在线出版日期: 2016-11-03（万方平台首次上网日期，不代表论文的发表时间）