SG-VAD: Stochastic Gates Based Speech Activity Detection

Jonathan Svirsky, Ofir Lindenbaum

Research output: Contribution to journalConference articlepeer-review


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.

Bibliographical note

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© 2023 IEEE.


  • Feature Selection
  • Speech Recognition
  • Voice Activity Detection


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