  The task of voice activity detection (VAD) is an often required module in
various speech processing, analysis and classification tasks. While
state-of-the-art neural network based VADs can achieve great results, they
often exceed computational budgets and real-time operating requirements. In
this work, we propose a computationally efficient real-time VAD network that
achieves state-of-the-art results on several public real recording datasets. We
investigate different training targets for the VAD and show that using the
segmental voice-to-noise ratio (VNR) is a better and more noise-robust training
target than the clean speech level based VAD. We also show that multi-target
training improves the performance further.
