KIsomap-Based Feature Extraction For Spoken Emotion Recognition

摘要：

Kernel-based feature extraction is a popular new research direction in machine learning community. Considering the nonlinear manifold structure of speech data, in this paper a recently proposed kernel manifold learning method, called kernel isometric mapping (KIsomap), is adopted as a mechanism for feature extraction on spoken emotion recognition tasks. KIsomap is used to extract the low-dimensional embedded feature data from original high-dimensional emotional acoustic features for spoken emotion recognition. Experimental results on the popular emotional German Berlin speech corpus show that KIsomap achieves better performance than other used two typical manifold learning methods, i.e., locally linear embedding (LLE) and isometric mapping (Isomap). recognition

关键词： kernel-based feature extraction manifold learning kernel isometric mapping spoken emotion

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: 1374-1377

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）