Boosting selection of speech related features to improve performance of multi-class SVMs in emotion detection

This paper deals with the strategies for feature selection and multi-class classification in the emotion detection problem. The aim is two-fold: to increase the effectiveness of four feature selection algorithms and to improve accuracy of multi-class classifiers for emotion detection problem under different frameworks and strategies. Although, a large amount of research has been conducted to determine the most informative features in emotion detection, it is still an open problem to identify reliably discriminating features. As it is believed that highly informative features are more critical factor than classifier itself, recent studies have been focused on identifying the features that contribute more to the classification problem. In this paper, in order to improve the performance of multi-class SVMs in emotion detection, 58 features extracted from recorded speech samples are processed in two new frameworks to boost the feature selection algorithms. Evaluation of the final feature sets validates that the frameworks are able to select more informative subset of the features in terms of class-separability. Also it is found that among four feature selection algorithms, a recently proposed one, LSBOUND, significantly outperforms the others. The accuracy rate obtained in the proposed framework is the highest achievement reported so far in the literature for the same dataset.