Ensemble softmax regression model for speech emotion recognition

Automatic emotion recognition from speech signals is one of the important research areas. Most speech emotion recognition methods have been proposed, among which ensemble learning is an effective way. However, they are still confronted with problems, such as the curse of dimensionality and the diversity of the base classifiers hardly ensured. To overcome the problems, this paper proposes an ensemble Softmax regression model for speech emotion recognition (ESSER). It applies the feature extraction methods with much different principles to generate the subspaces for the base classifier, so that the diversity of the base classifiers could be ensured. Furthermore, a feature selection method that selects features according to global structure of the data is used to reduce the dimension of subspaces, which can further increase the diversity of the base classifiers and overcome the curse of dimensionality. As in the case of the diversity of the base classifiers ensured, the performance of ensemble classifier highly depends on the ability of the base classifier, it is reasonable for ESSER to select Softmax as the base classifier as Softmax has shown its superiority in speech emotion recognition. The conducted experiments validate the proposed approach in term of the performance of speech emotion recognition.