  Traditional supervised voice activity detection (VAD) methods work well in
clean and controlled scenarios, with performance severely degrading in
real-world applications. One possible bottleneck is that speech in the wild
contains unpredictable noise types, hence frame-level label prediction is
difficult, which is required for traditional supervised VAD training. In
contrast, we propose a general-purpose VAD (GPVAD) framework, which can be
easily trained from noisy data in a weakly supervised fashion, requiring only
clip-level labels. We proposed two GPVAD models, one full (GPV-F), trained on
527 Audioset sound events, and one binary (GPV-B), only distinguishing speech
and noise. We evaluate the two GPV models against a CRNN based standard VAD
model (VAD-C) on three different evaluation protocols (clean, synthetic noise,
real data). Results show that our proposed GPV-F demonstrates competitive
performance in clean and synthetic scenarios compared to traditional VAD-C.
Further, in real-world evaluation, GPV-F largely outperforms VAD-C in terms of
frame-level evaluation metrics as well as segment-level ones. With a much lower
requirement for frame-labeled data, the naive binary clip-level GPV-B model can
still achieve comparable performance to VAD-C in real-world scenarios.
