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Entropy 2016, 18(8), 298; doi:10.3390/e18080298

Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine

1
Department of Electronics and Communication Engineering, K.L.N. College of Information Technology, Madurai 630612, India
2
Department of Electrical and Electronics Engineering, College of Engineering Pathanapuram, Kerala 689696, India
*
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Received: 27 April 2016 / Revised: 27 July 2016 / Accepted: 8 August 2016 / Published: 12 August 2016
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Abstract

This paper proposes support vector machine (SVM) based voice activity detection using FuzzyEn to improve detection performance under noisy conditions. The proposed voice activity detection (VAD) uses fuzzy entropy (FuzzyEn) as a feature extracted from noise-reduced speech signals to train an SVM model for speech/non-speech classification. The proposed VAD method was tested by conducting various experiments by adding real background noises of different signal-to-noise ratios (SNR) ranging from −10 dB to 10 dB to actual speech signals collected from the TIMIT database. The analysis proves that FuzzyEn feature shows better results in discriminating noise and corrupted noisy speech. The efficacy of the SVM classifier was validated using 10-fold cross validation. Furthermore, the results obtained by the proposed method was compared with those of previous standardized VAD algorithms as well as recently developed methods. Performance comparison suggests that the proposed method is proven to be more efficient in detecting speech under various noisy environments with an accuracy of 93.29%, and the FuzzyEn feature detects speech efficiently even at low SNR levels. View Full-Text
Keywords: voice activity detection; fuzzy entropy; support vector machine; k-NN voice activity detection; fuzzy entropy; support vector machine; k-NN
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Johny Elton, R.; Vasuki, P.; Mohanalin, J. Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine. Entropy 2016, 18, 298.

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