SFS feature selection technique for multistage emotion recognition

Abstract

Feature selection is very relevant for speech emotion recognition task. Still, there is no consensus on optimal feature set and classification scheme for this task. Sequential forward selection (SFS) technique for multistage emotion classification scheme is proposed in this paper. Feature sets were formed from initial collection of 6552 speech emotion features. Experimental study was performed using Berlin emotional speech and Lithuanian spoken language emotions databases. The proposed multistage classification scheme was compared with single stage scheme. Multistage scheme determined higher order of feature sets and demonstrated higher classification accuracy than single stage scheme by 0.5???4.3 %. The superiority of SFS technique against maximal individual efficiency and minimal cross-correlation selection criterions in multistage classification scheme was 20 % approximately.