Abstract
Speech emotion recognition is an interesting and challenging subject due to the emotion gap between speech signals and high-level speech emotion. To bridge this gap, this paper present a method of Chinese speech emotion recognition using Deep belief networks (DBN). DBN is used to perform unsupervised feature learning on the extracted low-level acoustic features. Then, Multi-layer Perceptron (MLP) is initialized in terms of the learning results of hidden layer of DBN, and employed for Chinese speech emotion classification. Experimental results on the Chinese Natural Audio-Visual Emotion Database (CHEAVD), show that the presented method obtains a classification accuracy of 32.80 % and macro average precision of 41.54 % on the testing data from the CHEAVD dataset on speech emotion recognition tasks, significantly outperforming the baseline results provided by the organizers in the speech emotion recognition sub-challenges.
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