Emotional Speech Classification Using Gaussian Mixture Models and the Sequential Floating Forward Selection Algorithm

Abstract

Emotional speech classification can be treated as a supervised learning task where the statistical properties of emotional speech segments are the features and the emotional styles form the labels. The Akaike criterion is used for estimating automatically the number of Gaussian densities that model the probability density function of the emotional speech features. A procedure for reducing the computational burden of crossvalidation in sequential floating forward selection algorithm is proposed that applies the t-test on the probability of correct classification for the Bayes classifier designed for various feature sets. For the Bayes classifier, the sequential floating forward selection algorithm is found to yield a higher probability of correct classification by 3% than that of the sequential forward selection algorithm either taking into account the gender information or ignoring it. The experimental results indicate that the utterances from isolated words and sentences are more colored emotional than those from paragraphs. Without taking into account the gender information, the probability of correct classification for the Bayes classifier admits a maximum when the probability density function of emotional speech features extracted from the aforementioned utterances is modeled as a mixture of 2 Gaussian densities