相空间重构的情感语音特征提取及优化

doi:10.3969/j.issn.1001-2400.2017.06.028

Abstract

Abstract:

In view of the imperfection of the existing speech emotional characteristics in the representation of emotional information, this paper introduces phase space reconstruction theory into the feature extraction of emotional speech. By analyzing the geometrical characteristics of phase space reconstruction under different speech emotion states, five nonlinear geometric features of trajectory-based descriptive contours under the reconstructed phase space are extracted as the new emotional speech characteristic parameters, and a novel feature parameter optimization method based on the relationship of emotional speech feature mapping is proposed. First, experience uses four basic emotions of happy, sad, neutral and angry in the German Berlin voice library as a sample. Second, the nonlinear geometric features and nonlinear attribute features (Minimum delay time, dimension correlation, Kolmogorov entropy, and Maximum Lyapunov exponent and Hurst exponent) are extracted from the emotional speech signal. Finally, a linear support vector machine (SVM) is employed to classify emotional speech signals according to the design scheme. The results show that the nonlinear geometric features have a strong dominance in the emotional speech recognition compared with the nonlinear attribute, and that the method of feature parameter optimization can obtain the optimal nonlinear feature set when nonlinear geometric features are combined with nonlinear attribute features,which verifies the practicability of the method.

Key words: phase space reconstruction, nonlinear geometric features, feature parameter optimization, speech emotion recognition

SUN Ying;SONG Chunxiao. Emotional speech feature extraction and optimization of phase space reconstruction[J].Journal of Xidian University, 2017, 44(6): 162-168.

References

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Emotional speech feature extraction and optimization of phase space reconstruction

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