Improvement of Noise-Robust Single-Channel Voice Activity Detection with Spatial Pre-processing

TL;DR

This paper enhances single-channel voice activity detection (VAD) in noisy environments by applying spatial pre-processing techniques, such as beamforming and spatial detection, leading to significant improvements over traditional methods and even multi-channel VAD in challenging conditions.

Contribution

The study introduces novel spatial pre-processing methods to improve single-channel VAD, demonstrating superior noise robustness compared to existing approaches.

Findings

Spatial detector significantly improves VAD accuracy.

Pre-processing methods outperform baseline MVAD in noisy conditions.

SVAD with spatial pre-processing is effective across various noise types.

Abstract

Voice activity detection (VAD) remains a challenge in noisy environments. With access to multiple microphones, prior studies have attempted to improve the noise robustness of VAD by creating multi-channel VAD (MVAD) methods. However, MVAD is relatively new compared to single-channel VAD (SVAD), which has been thoroughly developed in the past. It might therefore be advantageous to improve SVAD methods with pre-processing to obtain superior VAD, which is under-explored. This paper improves SVAD through two pre-processing methods, a beamformer and a spatial target speaker detector. The spatial detector sets signal frames to zero when no potential speaker is present within a target direction. The detector may be implemented as a filter, meaning the input signal for the SVAD is filtered according to the detector's output; or it may be implemented as a spatial VAD to be combined with the SVAD output. The evaluation is made on a noisy reverberant speech database, with clean speech from the Aurora 2 database and with white and babble noise. The results show that SVAD algorithms are significantly improved by the presented pre-processing methods, especially the spatial detector, across all signal-to-noise ratios. The SVAD algorithms with pre-processing significantly outperform a baseline MVAD in challenging noise conditions.