关键词: 语音情感识别, 听觉选择性注意, 语谱, 支持向量机

Abstract: The speech emotion recognition rate largely depends on the characteristic differences between different emotions. Through the analysis of time-frequency characteristics of speech and the simulation of the auditory selective attention mechanism, a speech emotion recognition algorithm is proposed based on the spectral feature. Firstly, based on the auditory selective attention mechanism, the speech signal is segmented, and the emotional saliency map is extracted from the time-frequency domain analysis of the segmented speech. Secondly, based on the saliency map, HU moment invariants features, texture features and some spectral features are used as the main features of speech emotion recognition. Finally, the speech emotion is recognized by the support vector machine. From the recognition results of emotional speech database, the proposed algorithm has higher speech emotion recognition rate and robustness, especially for the identification of practical irritable emotion. In addition, results of principal component analysis show that the characteristic differences between the selected emotions are more obvious and the algorithm is more practical.

Key words: speech emotion recognition, auditory selective attention, speech spectrum, support vector machine