  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.
