  Voice Activity Detection (VAD) aims at detecting speech segments on an audio
signal, which is a necessary first step for many today's speech based
applications. Current state-of-the-art methods focus on training a neural
network exploiting features directly contained in the acoustics, such as Mel
Filter Banks (MFBs). Such methods therefore require an extra normalisation step
to adapt to a new domain where the acoustics is impacted, which can be simply
due to a change of speaker, microphone, or environment. In addition, this
normalisation step is usually a rather rudimentary method that has certain
limitations, such as being highly susceptible to the amount of data available
for the new domain. Here, we exploited the crowd-sourced Common Voice (CV)
corpus to show that representations based on Self-Supervised Learning (SSL) can
adapt well to different domains, because they are computed with contextualised
representations of speech across multiple domains. SSL representations also
achieve better results than systems based on hand-crafted representations
(MFBs), and off-the-shelf VADs, with significant improvement in cross-domain
settings.
