Diversity subspace generation based on feature selection for speech emotion recognition.

Automatic emotion recognition from speech signals is an important research area. Many speech emotion recognition (SER) methods have been proposed, among which ensemble learning is an effective way to recognize speech emotion. However, the ability and diversity of the base classifier are not carefully considered in the case of limited available speech emotion samples. To overcome the above problem, this paper proposes a new diversity subspace generation based on feature selection (DSGFS) for SER. In DSGFS, a constrained problem is cleverly designed, which can iteratively select many diversity and strong subspaces, so the classification ability and the diversity of the corresponding base classifiers can be ensured. As a result, more features can be extracted from the data, an ensemble classifier framework with strong base classifiers can be automatically generated, and the number of base classifiers can be smaller. The proposed models offered SER weighted average recall of 87.24%, 64.58%, 69.10%, 53.50% on the EmoDB, SAVEE, RAVDESS, CASIA datasets with speaker independent, respectively, which validate the proposed approach in terms of the performance of speech emotion recognition. [ABSTRACT FROM AUTHOR]