# SG-VAD: Stochastic Gates Based Speech Activity Detection

arXiv: https://arxiv.org/abs/2210.16022 · HTML: https://mlsanity.com/paper/2210.16022/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2022.

## Built on by (most cited first, 5 of 5)

- [Sparse Binarization for Fast Keyword Spotting](https://mlsanity.com/paper/2406.06634/) (2024)
- [acoupi: An Open-Source Python Framework for Deploying Bioacoustic AI Models on Edge Devices](https://mlsanity.com/paper/2501.17841/) (2025)
- [Tiny Noise-Robust Voice Activity Detector for Voice Assistants](https://mlsanity.com/paper/2507.22157/) (2025)
- [Provable Speech Attributes Conversion via Latent Independence](https://mlsanity.com/paper/2510.05191/) (2025)
- [VAD to the Bone: Ultra-Tiny Speech Activity Detection for Edge Deployment](https://mlsanity.com/paper/2607.25870/) (2026)

## Abstract (arXiv)

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.