  In this work we present a novel single-channel Voice Activity Detector (VAD)
approach. We utilize a Convolutional Neural Network (CNN) which exploits the
spatial information of the noisy input spectrum to extract frame-wise embedding
sequence, followed by a Self Attention (SA) Encoder with a goal of finding
contextual information from the embedding sequence. Different from previous
works which were employed on each frame (with context frames) separately, our
method is capable of processing the entire signal at once, and thus enabling
long receptive field. We show that the fusion of CNN and SA architectures
outperforms methods based solely on CNN and SA. Extensive experimental-study
shows that our model outperforms previous models on real-life benchmarks, and
provides State Of The Art (SOTA) results with relatively small and lightweight
model.
