Discriminative weight training for a statistical model-based voice activity detection

Title
Discriminative weight training for a statistical model-based voice activity detection
Authors
Kang, S.I.; Jo, Q.H.; Chang, J.H.
Keywords
likelihood ratio, minimum classification error, voice activity detection
Issue Date
2008
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Abstract
In this letter, 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. That approach is different from that of previous works in that different weights are assigned to each frequency bin and is considered to be 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.
URI
http://dspace.inha.ac.kr/handle/10505/1834
ISSN
1070-9908
Appears in Collections:
College of Engineering(공과대학) > Electronic Engineering (전자공학) > Journal Papers, Reports(전자공학 논문, 보고서)
Files in This Item:
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