The Journal of the Acoustical Society of Korea (한국음향학회지)

The Acoustical Society of Korea (한국음향학회)
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Signal Subspace-based Voice Activity Detection Using Generalized Gaussian Distribution

일반화된 가우시안 분포를 이용한 신호 준공간 기반의 음성검출기법

  • 엄용섭 (전남대학교 전자컴퓨터공학부) ;
  • 장준혁 (한양대학교 전자전기공학부) ;
  • 김동국 (전남대학교 전자컴퓨터공학부)
  • Received : 2012.09.14
  • Accepted : 2012.10.19
  • Published : 2013.03.31
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Abstract

In this paper we propose an improved voice activity detection (VAD) algorithm using statistical models in the signal subspace domain. A uncorrelated signal subspace is generated using embedded prewhitening technique and the statistical characteristics of the noisy speech and noise are investigated in this domain. According to the characteristics of the signals in the signal subspace, a new statistical VAD method using GGD (Generalized Gaussian Distribution) is proposed. Experimental results show that the proposed GGD-based approach outperforms the Gaussian-based signal subspace method at 0-15 dB SNR simulation conditions.

본 논문에서는 신호준공간(signal subspace) 영역에서 통계적 모델을 이용한 향상된 음성검출기법을 제안한다. 이를 위해 EP(Embedded Prewhitening) 기법에 의해 비상관적인 (uncorrelated) 신호준공간을 생성하고, 이 영역에서 잡음음성과 잡음에 대한 통계적 특성을 파악하였다. 이러한 통계적 특성에 근거하여 GGD (Generalized Gaussian Distribution)을 사용하여 보다 효율적인 음성검출 알고리즘을 제안한다. 실험을 통해 제안된 기법이 0-15dB SNR의 시뮬레이션 환경에서 기존 Gaussian을 사용한 신호준공간 기법보다 향상된 음성검출 결과를 보여준다.

Keywords

References

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