Fast and accurate sequential floating forward feature selection with the Bayes classifier applied to speech emotion recognition

Abstract

This paper addresses subset feature selection performed by the sequential floating forward selection (SFFS). The criterion employed in SFFS is the correct classification rate of the Bayes classifier assuming that the features obey the multivariate Gaussian distribution. A theoretical analysis that models the number of correctly classified utterances as a hypergeometric random variable enables the derivation of an accurate estimate of the variance of the correct classification rate during cross-validation. By employing such variance estimate, we propose a fast SFFS variant. Experimental findings on Danish emotional speech (DES) and speech under simulated and actual stress (SUSAS) databases demonstrate that SFFS computational time is reduced by 50% and the correct classification rate for classifying speech into emotional states for the selected subset of features varies less than the correct classification rate found by the standard SFFS. Although the proposed SFFS variant is tested in the framework of speech emotion recognition, the theoretical results are valid for any classifier in the context of any wrapper algorithm.