대한전자공학회논문지SP (Journal of the Institute of Electronics Engineers of Korea SP)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

3GPP2 SMV의 실시간 유/무성음 분류 성능 향상을 위한 Gaussian Mixture Model 기반 연구

Enhancement Voiced/Unvoiced Sounds Classification for 3GPP2 SMV Employing GMM

  • 송지현 (인하대학교 전자공학부) ;
  • 장준혁 (인하대학교 전자공학부)
  • Song, Ji-Hyun (Department of Electronics Engineering, Inha University) ;
  • Chang, Joon-Hyuk (Department of Electronics Engineering, Inha University)
  • 발행 : 2008.09.25
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초록

본 논문에서는 패턴 인식에서 우수한 성능을 보이는 가우시안 혼합모델 (Gaussian mixture model, GMM)을 이용하여 비정상적인 잡음환경에서 3GPP2 selectable mode vocoder (SMV)의 유/무성음 분류 알고리즘 성능 향상을 위한 방법을 제안한다. 기존의 SMV에 대해서 분석하고, 이론 기반으로 유/무성음 분류 알고리즘에서 우수한 성능을 보여주는 특징 벡터를 선택하여 GMM의 입력벡터로 효과적으로 이용한다 다양한 잡음환경에서 시스템의 성능을 평가한 결과 GMM을 이용한 제안된 방법이 기존의 SMV의 방법보다 우수한 유/무성음 분류 성능을 보였다.

In this paper, we propose an approach to improve the performance of voiced/unvoiced (V/UV) decision under background noise environments for the selectable mode vocoder (SMV) of 3GPP2. We first present an effective analysis of the features and the classification method adopted in the SMV. And then feature vectors which are applied to the GMM are selected from relevant parameters of the SMV for the efficient voiced/unvoiced classification. For the purpose of evaluating the performance of the proposed algorithm, different experiments were carried out under various noise environments and yields better results compared with the conventional scheme of the SMV.

키워드

참고문헌

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