# Cross-domain Voice Activity Detection with Self-Supervised Representations

arXiv: https://arxiv.org/abs/2209.11061 · HTML: https://mlsanity.com/paper/2209.11061/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 22 Sep 2022.

## Builds on (most cited first, 12 of 12)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training](https://mlsanity.com/paper/2104.01027/) (2021)
- [LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech](https://mlsanity.com/paper/2104.11462/) (2021)
- [Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends](https://mlsanity.com/paper/2001.00378/) (2020)
- [End-to-End Automatic Speech Recognition Integrated With CTC-Based Voice Activity Detection](https://mlsanity.com/paper/2002.00551/) (2020)
- [The NIGENS General Sound Events Database](https://mlsanity.com/paper/1902.08314/) (2019)
- [Dual Attention in Time and Frequency Domain for Voice Activity Detection](https://mlsanity.com/paper/2003.12266/) (2020)
- [Statistical and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic Environments](https://mlsanity.com/paper/2005.09913/) (2020)
- [MLNET: An Adaptive Multiple Receptive-field Attention Neural Network for Voice Activity Detection](https://mlsanity.com/paper/2008.05650/) (2020)

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

- [Self-supervised Pretraining for Robust Personalized Voice Activity Detection in Adverse Conditions](https://mlsanity.com/paper/2312.16613/) (2023)

## Abstract (arXiv)

Voice Activity Detection (VAD) aims at detecting speech segments on an audio signal, which is a necessary first step for many today's speech based applications. Current state-of-the-art methods focus on training a neural network exploiting features directly contained in the acoustics, such as Mel Filter Banks (MFBs). Such methods therefore require an extra normalisation step to adapt to a new domain where the acoustics is impacted, which can be simply due to a change of speaker, microphone, or environment. In addition, this normalisation step is usually a rather rudimentary method that has certain limitations, such as being highly susceptible to the amount of data available for the new domain. Here, we exploited the crowd-sourced Common Voice (CV) corpus to show that representations based on Self-Supervised Learning (SSL) can adapt well to different domains, because they are computed with contextualised representations of speech across multiple domains. SSL representations also achieve better results than systems based on hand-crafted representations (MFBs), and off-the-shelf VADs, with significant improvement in cross-domain settings.