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Discriminative Weight Training for a Statistical Model-Based Voice Activity Detection  

Kang, Sang-Ick (인하대학교 전자전기공학부)
Jo, Q-Haing (인하대학교 전자전기공학부)
Park, Seung-Seop (서울대학교 전기컴퓨터공학부)
Chang, Joon-Hyuk (인하대학교 전자전기공학부)
Publication Information
The Journal of the Acoustical Society of Korea / v.26, no.5, 2007 , pp. 194-198 More about this Journal
Abstract
In this paper, we apply a discriminative weight training to a statistical model-based voice activity detection(VAD). In our approach, the VAD decision rule is expressed as the geometric mean of optimally weighted likelihood ratios(LRs) based on a minimum classification error(MCE) method which is different from the previous works in that different weights are assigned to each frequency bin which is considered more realistic. According to the experimental results, the proposed approach is found to be effective for the statistical model-based VAD using the LR test.
Keywords
Voice activity detection; Minimum classification error; Statistical model; Likelihood ratio;
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