Voice-Activity Detection Using Long-Term Sub-Band Entropy Measure

被引：0
|
作者：
Wang, Kun-Ching [1 ]
机构：
[1] Shin Chien Univ, Taipei, Taiwan
来源：
IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES | 2012年 / E95A卷 / 09期
关键词：
voice activity detection; long-term spectral analysis; sub-band entropy; variable-level noise; SPEECH RECOGNITION; WORD RECOGNITION; NOISE;
D O I：
10.1587/transfun.E95.A.1606
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
A novel long-term sub-band entropy (LT-SubEntropy) measure, which uses improved long-term spectral analysis and sub-band entropy, is proposed for voice activity detection (VAD). Based on the measure, we can accurately exploit the inherent nature of the formant structure on speech spectrogram (the well-known as voiceprint). Results show that the proposed VAD is superior to existing standard VAD methods at low SNR levels, especially at variable-level noise.
引用
收藏
页码：1606 / 1609
页数：4
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