Four-stage feature selection to recognize emotion from speech signals

Feature selection plays an important role in emotion recognition from speech signals because it improves the classification accuracy by choosing the best uncorrelated features. In wrapper method of feature selection, the features are evaluated by a classifier. Features of large dimension will increase the computational complexity of the classifier, and further it will affect the training of classifiers which needs inverse of covariance matrix. We propose a four-stage feature selection method which avoids the problem of curse of dimensionality by the principle of divide and conquer. In the proposed method, the dimension of the feature vector is shortened at any stage in a way that the classifiers, whose training is affected by the large feature dimension, can also be used to evaluate the features. Experimental results show that the four-stage feature selection method improves classification accuracy. Another method to improve classification accuracy is evolved by bringing together several classifiers with a fusion technique. Class-specific multiple classifiers scheme is one such method that improves classification accuracy by combining optimum performance feature set and classifier for each emotional class. In this work, we improve the performance of the class-specific multiple classifiers scheme by embedding the proposed feature selection method in its structure.