Vocal Effort Detection Based on Spectral Information Entropy and Complementary Models

doi:10.13190/j.jbupt.2017-157

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2018, Vol. 41 ›› Issue (2): 56-61.doi: 10.13190/j.jbupt.2017-157

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Vocal Effort Detection Based on Spectral Information Entropy and Complementary Models

CHAO Hao, LU Bao-yun, LIU Yong-li, LIU Zhi-zhong, SONG Cheng

School of Computer Science and Technology, Henan Polytechnic University, Henan Jiaozuo 454000, China

Received:2017-08-10 Online:2018-04-28 Published:2018-03-17

Abstract

Abstract: A new vocal effort detection method based on model fusion was presented. By analyzing the ability to discriminate the vocal effort modes, the spectral information entropy feature which contains more salient information regarding the vocal effort level was proposed. Then, the complementary models were presented to achieve the fusion of the spectrum features, Mel-frequency cepstral coefficients and spectral information entropy feature. Experiments are conducted on isolated words test set, and the proposed method achieves 81.6% average recognition accuracy. The results show the spectral information entropy has the best performance among the three kinds of features and the complementary models can integrate the three kinds of features effectively.

Key words: vocal effort, spectral information entropy, support vector machine, Gaussian mixture model, multilayer perceptron

CLC Number:

TN391.42

CHAO Hao, LU Bao-yun, LIU Yong-li, LIU Zhi-zhong, SONG Cheng. Vocal Effort Detection Based on Spectral Information Entropy and Complementary Models[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 41(2): 56-61.

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Vocal Effort Detection Based on Spectral Information Entropy and Complementary Models

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