Robust emotion recognition in noisy speech via sparse representation

Emotion recognition in speech signals is currently a very active research topic and has attracted much attention within the engineering application area. This paper presents a new approach of robust emotion recognition in speech signals in noisy environment. By using a weighted sparse representation model based on the maximum likelihood estimation, an enhanced sparse representation classifier is proposed for robust emotion recognition in noisy speech. The effectiveness and robustness of the proposed method is investigated on clean and noisy emotional speech. The proposed method is compared with six typical classifiers, including linear discriminant classifier, K-nearest neighbor, C4.5 decision tree, radial basis function neural networks, support vector machines as well as sparse representation classifier. Experimental results on two publicly available emotional speech databases, that is, the Berlin database and the Polish database, demonstrate the promising performance of the proposed method on the task of robust emotion recognition in noisy speech, outperforming the other used methods.