Vocal Effort Detection Based on Spectral Information Entropy Feature and Model Fusion


Hao Chao, Bao-Yun Lu, Yong-Li Liu, Hui-Lai Zhi, Journal of Information Processing Systems Vol. 14, No. 1, pp. 218-227, Jan. 2018

10.3745/JIPS.04.0063
Keywords: Gaussian mixture model, Model Fusion, Multilayer Perceptron, Spectral Information Entropy, Support Vector Machine, Vocal Effort
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Abstract

Vocal effort detection is important for both robust speech recognition and speaker recognition. In this paper, the spectral information entropy feature which contains more salient information regarding the vocal effort level is firstly proposed. Then, the model fusion method based on complementary model is presented to recognize vocal effort level. Experiments are conducted on isolated words test set, and the results show the spectral information entropy has the best performance among the three kinds of features. Meanwhile, the recognition accuracy of all vocal effort levels reaches 81.6%. Thus, potential of the proposed method is demonstrated


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Cite this article
[APA Style]
Hao Chao, Bao-Yun Lu, Yong-Li Liu, & Hui-Lai Zhi (2018). Vocal Effort Detection Based on Spectral Information Entropy Feature and Model Fusion . Journal of Information Processing Systems, 14(1), 218-227. DOI: 10.3745/JIPS.04.0063.

[IEEE Style]
H. Chao, B. Lu, Y. Liu and H. Zhi, "Vocal Effort Detection Based on Spectral Information Entropy Feature and Model Fusion ," Journal of Information Processing Systems, vol. 14, no. 1, pp. 218-227, 2018. DOI: 10.3745/JIPS.04.0063.

[ACM Style]
Hao Chao, Bao-Yun Lu, Yong-Li Liu, and Hui-Lai Zhi. 2018. Vocal Effort Detection Based on Spectral Information Entropy Feature and Model Fusion . Journal of Information Processing Systems, 14, 1, (2018), 218-227. DOI: 10.3745/JIPS.04.0063.