  This paper presents a novel framework for Speech Activity Detection (SAD).
Inspired by the recent success of multi-task learning approaches in the speech
processing domain, we propose a novel joint learning framework for SAD. We
utilise generative adversarial networks to automatically learn a loss function
for joint prediction of the frame-wise speech/ non-speech classifications
together with the next audio segment. In order to exploit the temporal
relationships within the input signal, we propose a temporal discriminator
which aims to ensure that the predicted signal is temporally consistent. We
evaluate the proposed framework on multiple public benchmarks, including NIST
OpenSAT' 17, AMI Meeting and HAVIC, where we demonstrate its capability to
outperform state-of-the-art SAD approaches. Furthermore, our cross-database
evaluations demonstrate the robustness of the proposed approach across
different languages, accents, and acoustic environments.
