Multiple Feature Extraction and Hierarchical Classifiers for Emotions Recognition

The recognition of the emotional states of speaker is a multidisciplinary research area that has received great interest in the last years. One of the most important goals is to improve the voiced-based human-machine interactions. Recent works on this domain use the prosodic features and the spectrum characteristics of speech signal, with standard classifier methods. Furthermore, for traditional methods the improvement in performance has also found a limit. In this paper, the spectral characteristics of emotional signals is used in order to group emotions. Standard classifiers based on Gaussian Mixture Models, Hidden Markov Models and Multilayer Perceptron are tested. These classifiers have been evaluated in different configurations with different features, in order to design a new hierarchical method for emotions classification. The proposed multiple feature hierarchical method improves the performance in 6.35% over the standard classifiers.