KIsomap-based feature extraction for spoken emotion recognition

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).