Spoken Emotion Recognition Using Local Fisher Discriminant Analysis

摘要：

Spoken emotion recognition is an interesting and challenging subject. In this paper, a new feature extraction method based on local Fisher discriminant analysis (LFDA) is proposed for spoken emotion recognition. LFDA is used to extract the low-dimensional discriminant embedded feature data from high-dimensional emotional speech features on spoken emotion recognition tasks. The performance of LFDA is compared with principal component analysis (PCA) and linear discriminant analysis (LDA). Experimental results on the emotional Chinese speech database demonstrate the promising performance of the proposed method.

关键词： spoken emotion recognition feature extraction local Fisher discriminant analysis

作者: Shiqing Zhang Bicheng Lei Aihua Chen Caiming Chen Yuefen Chen

作者单位: School of Physics and Electronic Engineering Taizhou University Taizhou, China

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 538-540

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）