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We propose a novel voice activity detection (VAD) model in a low-resourceenvironment. Our key idea is to model VAD as a denoising task, and construct anetwork that is designed to identify nuisance features for a speechclassification task. We train the model to simultaneously identify irrelevantfeatures while predicting the type of speech event. Our model contains only7.8K parameters, outperforms the previously proposed methods on the AVA-Speechevaluation set, and provides comparative results on the HAVIC dataset. Wepresent its architecture, experimental results, and ablation study on themodel's components. We publish the code and the models herehttps://www.github.com/jsvir/vad.

SG-VAD: Stochastic Gates Based Speech Activity Detection