Support vector machines for emotion recognition in Chinese speech

摘要

Support vector machines (SVMs) are utilized for emotion recognition in Chinese speech in this paper. Both binary-class discrimination and the multi-class discrimination are discussed. It proves that the emotional features construct a nonlinear problem in the input space, and SVMs based on nonlinear mapping can solve it more effectively than other linear methods. Multi-class classification based on SVMs with a soft decision function is constructed to classify the four emotion situations. Compared with principal component analysis (PCA) method and modified PCA method, SVMs perform the best result in multi-class discrimination by using nonlinear kernel mapping.