Adversarial Multi-Task Deep Learning for Noise-Robust Voice Activity Detection with Low Algorithmic Delay

TL;DR

This paper introduces an adversarial multi-task learning approach to improve noise-robust voice activity detection, maintaining low latency and enhancing performance in noisy environments without increasing testing complexity.

Contribution

It proposes a novel adversarial multi-task training method for VAD that enhances noise robustness and latency performance without additional testing costs.

Findings

Adversarial training improves AUC in noisy conditions.

Performance remains stable at higher SNR levels.

Moderate degradation occurs when reducing algorithmic delay from 398 ms to 23 ms.

Abstract

Voice Activity Detection (VAD) is an important pre-processing step in a wide variety of speech processing systems. VAD should in a practical application be able to detect speech in both noisy and noise-free environments, while not introducing significant latency. In this work we propose using an adversarial multi-task learning method when training a supervised VAD. The method has been applied to the state-of-the-art VAD Waveform-based Voice Activity Detection. Additionally the performance of the VADis investigated under different algorithmic delays, which is an important factor in latency. Introducing adversarial multi-task learning to the model is observed to increase performance in terms of Area Under Curve (AUC), particularly in noisy environments, while the performance is not degraded at higher SNR levels. The adversarial multi-task learning is only applied in the training phase and thus introduces no additional cost in testing. Furthermore the correlation between performance and algorithmic delays is investigated, and it is observed that the VAD performance degradation is only moderate when lowering the algorithmic delay from 398 ms to 23 ms.