Comparing feature dimension reduction algorithms for GMM-SVM based speech emotion recognition

How to select effective emotional features are important for improving the performance of automatic speech emotion recognition. Although various feature dimension reduction algorithms were put forward that could help gain the accuracy rate of emotion distinction, but most of them exist various defects, such as high negative impact of the recognition rate, high computational complexity. Regarding this, two dimension reduction algorithms based on PCA (principal component analysis) and KPCA (Kernel-PCA) were comparatively discussed in this paper. The original features extracted from databases were transformed by PCA or KPCA. The weights of these new features over the transforming matrix were calculated and ranked, based on which features were chosen. Experimental results show that feature dimension reduction can make principal contribution to the accuracy of speech emotion recognition, and KPCA slightly outperforms PCA on the hit rate and the remaining dimensions.