关键词: 相空间重构, 情感语音识别, 非线性几何特征, 特征选择, 最优特征集合

Abstract: Aiming at addressing the limitations of the existing time domain and frequency domain attribute characteristics in distinguishing the emotional state, a nonlinear geometric feature extraction method based on phase space reconstruction theory is proposed. Firstly, the phase space is reconstructed by analyzing the minimum delay time and the embedded dimension of the emotion speech signal. Secondly, five kinds of nonlinear geometric features based on trajectory contour are analyzed and extracted under reconstructed phase space. Finally, combining with the prosody features, MFCC features and chaotic characteristics, the experiments are designed to verify the ability of the proposed feature to distinguish the emotional state and to obtain the complete set of optimal features of the emotional information through the feature selection. Five kinds of emotions （happy, sad, neutral, angry and fear） in the German speech library are selected as the experimental data source, and the support vector machine is used as the identification network. The experimental results show that the proposed feature can not only characterize the emotion difference in the speech signal, but also make up the deficiency of the existing feature in characterizing the emotional state compared with the prosody feature, the MFCC feature and the chaotic feature.

Key words: phase space reconstruction, emotional speech recognition, nonlinear geometric features, feature selection, optimal feature set