Voice Activity Detection for Monaural Speech Enhancement Using Visual Cues

2021 ◽  
pp. 251-258
Author(s):  
S. Balasubramanian ◽  
R. Rajavel ◽  
S. Shoba
Keyword(s):  
Speech Enhancement ◽  
Visual Cues ◽  
Voice Activity Detection ◽  
Activity Detection ◽  
Voice Activity

scholarly journals Power Spectral Deviation-Based Voice Activity Detection Incorporating Teager Energy for Speech Enhancement

Symmetry ◽  
2016 ◽  
Vol 8 (7) ◽  
pp. 58 ◽  
Author(s):  
Sang-Kyun Kim ◽  
Sang-Ick Kang ◽  
Young-Jin Park ◽  
Sanghyuk Lee ◽  
Sangmin Lee
Keyword(s):  
Speech Enhancement ◽  
Voice Activity Detection ◽  
Activity Detection ◽  
Power Spectral ◽  
Teager Energy ◽  
Voice Activity

scholarly journals A Hierarchical Framework Approach for Voice Activity Detection and Speech Enhancement

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yan Zhang ◽  
Zhen-min Tang ◽  
Yan-ping Li ◽  
Yang Luo
Keyword(s):  
Speech Enhancement ◽  
Speaker Recognition ◽  
Wiener Filter ◽  
Voice Activity Detection ◽  
Activity Detection ◽  
Hierarchical Framework ◽  
Framework Approach ◽  
Noisy Conditions ◽  
Voice Activity ◽  
Timit Database

Accurate and effective voice activity detection (VAD) is a fundamental step for robust speech or speaker recognition. In this study, we proposed a hierarchical framework approach for VAD and speech enhancement. The modified Wiener filter (MWF) approach is utilized for noise reduction in the speech enhancement block. For the feature selection and voting block, several discriminating features were employed in a voting paradigm for the consideration of reliability and discriminative power. Effectiveness of the proposed approach is compared and evaluated to other VAD techniques by using two well-known databases, namely, TIMIT database and NOISEX-92 database. Experimental results show that the proposed method performs well under a variety of noisy conditions.

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