  Voice activity detection is the task of detecting speech regions in a given
audio stream or recording. First, we design a neural network combining
trainable filters and recurrent layers to tackle voice activity detection
directly from the waveform. Experiments on the challenging DIHARD dataset show
that the proposed end-to-end model reaches state-of-the-art performance and
outperforms a variant where trainable filters are replaced by standard cepstral
coefficients. Our second contribution aims at making the proposed voice
activity detection model robust to domain mismatch. To that end, a domain
classification branch is added to the network and trained in an adversarial
manner. The same DIHARD dataset, drawn from 11 different domains is used for
evaluation under two scenarios. In the in-domain scenario where the training
and test sets cover the exact same domains, we show that the domain-adversarial
approach does not degrade performance of the proposed end-to-end model. In the
out-domain scenario where the test domain is different from training domains,
it brings a relative improvement of more than 10%. Finally, our last
contribution is the provision of a fully reproducible open-source pipeline than
can be easily adapted to other datasets.
