  We propose a novel voice activity detection (VAD) model in a low-resource
environment. Our key idea is to model VAD as a denoising task, and construct a
network that is designed to identify nuisance features for a speech
classification task. We train the model to simultaneously identify irrelevant
features while predicting the type of speech event. Our model contains only
7.8K parameters, outperforms the previously proposed methods on the AVA-Speech
evaluation set, and provides comparative results on the HAVIC dataset. We
present its architecture, experimental results, and ablation study on the
model's components. We publish the code and the models here
https://www.github.com/jsvir/vad.
