Voice activity detection based on statistical models and machine learning approaches

  • Authors:

  • Jong Won Shin;Joon-Hyuk Chang;Nam Soo Kim

  • Affiliations:

  • School of Electrical Engineering and INMC, Seoul National University, Seoul 151-742, Republic of Korea;School of Electronic Engineering, Inha University, 253 Yonghyeon-dong, Nam-gu, Incheon 401-751, Republic of Korea;School of Electrical Engineering and INMC, Seoul National University, Seoul 151-742, Republic of Korea

  • Venue:
  • Computer Speech and Language
  • Year:
  • 2010

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Abstract

The voice activity detectors (VADs) based on statistical models have shown impressive performances especially when fairly precise statistical models are employed. Moreover, the accuracy of the VAD utilizing statistical models can be significantly improved when machine-learning techniques are adopted to provide prior knowledge for speech characteristics. In the first part of this paper, we introduce a more accurate and flexible statistical model, the generalized gamma distribution (G@CD) as a new model in the VAD based on the likelihood ratio test. In practice, parameter estimation algorithm based on maximum likelihood principle is also presented. Experimental results show that the VAD algorithm implemented based on G@CD outperform those adopting the conventional Laplacian and Gamma distributions. In the second part of this paper, we introduce machine learning techniques such as a minimum classification error (MCE) and support vector machine (SVM) to exploit automatically prior knowledge obtained from the speech database, which can enhance the performance of the VAD. Firstly, we present a discriminative weight training method based on the MCE criterion. In this approach, the VAD decision rule becomes the geometric mean of optimally weighted likelihood ratios. Secondly, the SVM-based approach is introduced to assist the VAD based on statistical models. In this algorithm, the SVM efficiently classifies the input signal into two classes which are voice active and voice inactive regions with nonlinear boundary. Experimental results show that these training-based approaches can effectively enhance the performance of the VAD.