New frameworks to boost feature selection algorithms in emotion detection for improved human-computer interaction

One of the primary aims in human-computer interaction research is to develop an ability to recognize affective state of the user. Such ability is indispensable to have a more human-like nature in human-computer interaction. However, the researches in this direction are not mature and intensive efforts have only been witnessed recently. This work envisages the possibility of enhancing feature selection phase of emotion detection task to obtain robust parameters which will be determined from verbal information to achieve an improved affective human-computer interaction. As highly informative feature selection is believed to be a more critical factor than classifier itself, recent studies have increasingly focussed on determining features that contribute more to the classification problem. Two new frameworks for multi-class emotion detection problem are proposed in this paper, so as to boost the feature selection algorithms in a way that the selected features will be more informative in terms of class-separability. Evaluation of the selected final features is accomplished by multi-class classifiers. Results show that the proposed frameworks are successful in terms of attaining lower average cross-validation error.