Improving speech emotion recognition using adaptive genetic algorithms

Several methods for automatic classification of utterances into emotional states have been proposed. However, the reported error rates are rather high, far behind the word error rates in speech recognition. Accordingly, there is a constant motivation for performance optimization. In this paper, self-adaptive genetic algorithms are employed to search for the worst performing features with respect to the probability of correct classification achieved by the Bayes classifier in a first stage. That is, a genetic algorithm-based implementation of backward feature selection is proposed. These features are subsequently excluded from sequential floating feature selection employing the probability of correct classification achieved by the Bayes classifier as criterion. In a second stage, self-adaptive genetic algorithms are employed to search for the worst performing utterances with respect to the same criterion. The sequential application of both stages is demonstrated to improve speech emotion recognition on the Danish Emotional Speech database.