Speech Emotion Recognition Based on a Fusion of All-Class and Pairwise-Class Feature Selection

Traditionally in speech emotion recognition, feature selection(FS) is implemented by considering the features from all classes jointly. In this paper, a hybrid system based on all-class FS and pairwise-class FS is proposed to improve speech emotion classification performance. Besides a subset of features obtained from an all-class structure, FS is performed on the available data from each pair of classes. All these subsets are used in their corresponding K-nearest-neighbors(KNN) or Support Vector Machine(SVM) classifiers and the posterior probabilities of the multi-classifiers are fused hierarchically. The experiment results demonstrate that compared with the classical method based on all-class FS and the pairwise method based on pairwise-class FS, the proposed approach achieves 3.2%-8.4% relative improvement on the average F1-measure in speaker-independent emotion recognition.