# Tiny Noise-Robust Voice Activity Detector for Voice Assistants

arXiv: https://arxiv.org/abs/2507.22157 · HTML: https://mlsanity.com/paper/2507.22157/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 6 of 6)

- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [A scalable noisy speech dataset and online subjective test framework](https://mlsanity.com/paper/1909.08050/) (2019)
- [MarbleNet: Deep 1D Time-Channel Separable Convolutional Neural Network for Voice Activity Detection](https://mlsanity.com/paper/2010.13886/) (2020)
- [AVA-Speech: A Densely Labeled Dataset of Speech Activity in Movies](https://mlsanity.com/paper/1808.00606/) (2018)
- [On training targets for noise-robust voice activity detection](https://mlsanity.com/paper/2102.07445/) (2021)
- [SG-VAD: Stochastic Gates Based Speech Activity Detection](https://mlsanity.com/paper/2210.16022/) (2022)

## Built on by (most cited first, 1 of 1)

- [HyWA: Architecture-Preserving Personalized Voice Activity Detection for Full-Duplex Voice Assistants](https://mlsanity.com/paper/2510.12947/) (2025)

## Abstract (arXiv)

Voice Activity Detection (VAD) in the presence of background noise remains a challenging problem in speech processing. Accurate VAD is essential in automatic speech recognition, voice-to-text, conversational agents, etc, where noise can severely degrade the performance. A modern application includes the voice assistant, specially mounted on Artificial Intelligence of Things (AIoT) devices such as cell phones, smart glasses, earbuds, etc, where the voice signal includes background noise. Therefore, VAD modules must remain light-weight due to their practical on-device limitation. The existing models often struggle with low signal-to-noise ratios across diverse acoustic environments. A simple VAD often detects human voice in a clean environment, but struggles to detect the human voice in noisy conditions. We propose a noise-robust VAD that comprises a light-weight VAD, with data pre-processing and post-processing added modules to handle the background noise. This approach significantly enhances the VAD accuracy in noisy environments and requires neither a larger model, nor fine-tuning. Experimental results demonstrate that our approach achieves a notable improvement compared to baselines, particularly in environments with high background noise interference. This modified VAD additionally improving clean speech detection.