A Hierarchical Framework for Speech Emotion Recognition

Dimensionality reduction is an important issue in pattern recognition. Two popular methods used in this field are principal component analysis (PCA) and linear discriminant analysis (LDA). In this paper, detailed comparisons were performed among PCA, LDA and PCA+LDA considering the lack of similar studies. It showed that no particular method was optimal across all emotion categories. Based on this analysis, a new framework combining PCA and LDA was proposed. An appropriate dimensionality reduction method was employed for every emotion category in the new framework. Experimental results demonstrate that our approach achieves a better overall performance compared with PCA, LDA or PCA+LDA