COMBINING FEATURE SELECTION AND REPRESENTATION FOR SPEECH EMOTION RECOGNITION

In this paper, we propose a feature selection and representation combination method to generate discriminative features for speech emotion recognition. In feature selection stage, a Multiple Kernel Learning (MKL) based strategy is used to obtain the optimal feature subset. Specifically, features selected at least n times among 10-fold cross validation are collected to build a new feature subset named n-subset, then the n-subset resulting in the highest classification accuracy is viewed as the optimal one. In feature representation stage, the optimal feature subset is mapped to a hidden representation using a denoising autoencoder (DAE). The model parameters are learned by minimizing the squared error between the original and the reconstructed input. The hidden representation is then used as the final feature set in the MKL model for emotion recognition. Our experimental results show significant performance improvement compared to using the original features in both of the inner-corpus and cross-corpus scenarios.