Spoken Emotion Recognition Using Local Fisher Discriminant Analysis

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.