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.

Optimizing Voice Activity Detection for Noisy Conditions

2019 ◽  
Author(s):  
Ruixi Lin ◽  
Charles Costello ◽  
Charles Jankowski ◽  
Vishwas Mruthyunjaya
Keyword(s):  
Voice Activity Detection ◽  
Activity Detection ◽  
Noisy Conditions ◽  
Voice Activity

Speaker Verification Under Degraded Conditions Using Empirical Mode Decomposition Based Voice Activity Detection Algorithm

2014 ◽  
Vol 23 (4) ◽  
pp. 359-378
Author(s):  
M. S. Rudramurthy ◽  
V. Kamakshi Prasad ◽  
R. Kumaraswamy
Keyword(s):  
Speaker Recognition ◽  
Speaker Verification ◽  
Signal To Noise Ratio ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Voice Activity Detection ◽  
Activity Detection ◽  
Front End ◽  
Different Types ◽  
Voice Activity

AbstractThe performance of most of the state-of-the-art speaker recognition (SR) systems deteriorates under degraded conditions, owing to mismatch between the training and testing sessions. This study focuses on the front end of the speaker verification (SV) system to reduce the mismatch between training and testing. An adaptive voice activity detection (VAD) algorithm using zero-frequency filter assisted peaking resonator (ZFFPR) was integrated into the front end of the SV system. The performance of this proposed SV system was studied under degraded conditions with 50 selected speakers from the NIST 2003 database. The degraded condition was simulated by adding different types of noises to the original speech utterances. The different types of noises were chosen from the NOISEX-92 database to simulate degraded conditions at signal-to-noise ratio levels from 0 to 20 dB. In this study, widely used 39-dimension Mel frequency cepstral coefficient (MFCC; i.e., 13-dimension MFCCs augmented with 13-dimension velocity and 13-dimension acceleration coefficients) features were used, and Gaussian mixture model–universal background model was used for speaker modeling. The proposed system’s performance was studied against the energy-based VAD used as the front end of the SV system. The proposed SV system showed some encouraging results when EMD-based VAD was used at its front end.

Sign in / Sign up

Export Citation Format

Share Document