A New Method Based on HMMs and K-Means Algorithms for Noise-Robust Voice Activity Detector

Abstract:

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In this paper, we proposed left-right hidden Markov models (HMMs) combination with k-means threshold of Likelihood ratio test (LRT) to identify the start and end of the speech. This method builds two models of non-speech and speech but not two states, i.e. each model could conclude several states. In the experiments we present the Voice Activity Detection (VAD) results between two states hidden semi-Markov model (HSMM) and proposed algorithm. We also compare accuracy and robust between the k-means threshold and the adaptive threshold in high signal to noise rate in the background noise. It presents that k-means threshold is more effective than the adaptive threshold and the proposed method also make a better performance than two states HSMM based VAD, especially in the low signal-to-noise ratio (SNR) environment.

Info:

Periodical:

Edited by:

Zhixiang Hou

Pages:

461-464

DOI:

10.4028/www.scientific.net/AMM.128-129.461

Citation:

B. Luo et al., "A New Method Based on HMMs and K-Means Algorithms for Noise-Robust Voice Activity Detector", Applied Mechanics and Materials, Vols. 128-129, pp. 461-464, 2012

Online since:

October 2011

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$35.00

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