情感语音的非线性动力学特征

doi:10.3969/j.issn.1001-2400.2016.05.029

Abstract

Abstract:

The application of nonlinear measures based on the chaotic characteristics of emotional speech is proposed. Nonlinear features such as minimum delay time, dimension correlation, Kolmogorov entropy, Lyapunov exponent and Hurst exponent are extracted from the emotional speech signal. The performance of nonlinear features is verified by the comparisons of recognition rates of different features (nonlinear characteristics, prosodic features and MFCC features). First, the Berlin emotional speech database and TYUT2.0 emotional speech database are chosen as the corpus independently, both covering three emotional classifications (anger, happiness and fear). The effectiveness of the nonlinear characteristics is tested on the Support Vector Machine Network. The result shows that the performance of nonlinear features outperforms that of prosodic features on the Berlin emotional speech database and that of prosodic features and MFCC on TYUT2.0 emotional speech database. In addition, nonlinear features have obvious advantage in detecting more natural emotional speech and better robustness.

Key words: emotional speech recognition, chaos theory, nonlinear features, dynamic model

YAO Hui;SUN Ying;ZHANG Xueying. Research on nonlinear dynamics features of emotional speech[J].Journal of Xidian University, 2016, 43(5): 167-172.

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Research on nonlinear dynamics features of emotional speech

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