Robust Voice Activity Detection Using Long-Term Signal Variability

Authors:
P. K. Ghosh;A. Tsiartas;S. Narayanan
Affiliations:
Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA, USA;-;-
Venue:
IEEE Transactions on Audio, Speech, and Language Processing
Year:
2011

Citing 0
Cited 9

Robustness of group delay representations for noisy speech signals

International Journal of Speech Technology
Behavioral signal processing for understanding (distressed) dyadic interactions: some recent developments

J-HGBU '11 Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding
Toward automating a human behavioral coding system for married couples' interactions using speech acoustic features

Speech Communication
Voice activity detection algorithm using nonlinear spectral weights, hangover and hangbefore criteria

Computers and Electrical Engineering
Compressive speech enhancement

Speech Communication
CoenoFire: monitoring performance indicators of firefighters in real-world missions using smartphones

Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
Robust voice activity detection for social sensing

Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
A study of voice activity detection techniques for NIST speaker recognition evaluations

Computer Speech and Language
Computing vocal entrainment: A signal-derived PCA-based quantification scheme with application to affect analysis in married couple interactions

Computer Speech and Language

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a novel long-term signal variability (LTSV) measure, which describes the degree of nonstationarity of the signal. We analyze the LTSV measure both analytically and empirically for speech and various stationary and nonstationary noises. Based on the analysis, we find that the LTSV measure can be used to discriminate noise from noisy speech signal and, hence, can be used as a potential feature for voice activity detection (VAD). We describe an LTSV-based VAD scheme and evaluate its performance under eleven types of noises and five types of signal-to-noise ratio (SNR) conditions. Comparison with standard VAD schemes demonstrates that the accuracy of the LTSV-based VAD scheme averaged over all noises and all SNRs is ~6% (absolute) better than that obtained by the best among the considered VAD schemes, namely AMR-VAD2. We also find that, at -10 dB SNR, the accuracies of VAD obtained by the proposed LTSV-based scheme and the best considered VAD scheme are 88.49% and 79.30%, respectively. This improvement in the VAD accuracy indicates the robustness of the LTSV feature for VAD at low SNR condition for most of the noises considered.