A Novel Speech Emotion Recognition Method via Incomplete Sparse Least Square Regression

In this letter, we propose a novel speech emotion recognition method based on least square regression (LSR) model, in which a novel incomplete sparse LSR (ISLSR) model is proposed and utilized to characterize the linear relationship between speech features and the corresponding emotion labels. In training the ISLSR model, both labeled and unlabeled speech data sets are utilized, where the use of unlabeled data set aims to enhance the compatibility of the model such that it is well suitable for the out-of-sample speech data. Another novelty of ISLSR lies in the capability of dealing with feature selection. To evaluate the performance of the proposed method, we conduct experiments on two emotional speech databases. The experimental results on both databases demonstrate that the proposed method achieves better recognition performance in compared with several state-of-the-art methods.