# VSEGAN: Visual Speech Enhancement Generative Adversarial Network

arXiv: https://arxiv.org/abs/2102.02599 · HTML: https://mlsanity.com/paper/2102.02599/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2021.

## Built on by (most cited first, 4 of 4)

- [VoViT: Low Latency Graph-based Audio-Visual Voice Separation Transformer](https://mlsanity.com/paper/2203.04099/) (2022)
- [LA-VocE: Low-SNR Audio-visual Speech Enhancement using Neural Vocoders](https://mlsanity.com/paper/2211.10999/) (2022)
- [AUREXA-SE: Audio-Visual Unified Representation Exchange Architecture with Cross-Attention and Squeezeformer for Speech Enhancement](https://mlsanity.com/paper/2510.05295/) (2025)
- [Efficient Audio-Visual Speech Enhancement via Reinforcement Learning-Based Neural Architecture Search](https://mlsanity.com/paper/2610.12796/) (2026)

## Abstract (arXiv)

Speech enhancement is an essential task of improving speech quality in noise scenario. Several state-of-the-art approaches have introduced visual information for speech enhancement,since the visual aspect of speech is essentially unaffected by acoustic environment. This paper proposes a novel frameworkthat involves visual information for speech enhancement, by in-corporating a Generative Adversarial Network (GAN). In par-ticular, the proposed visual speech enhancement GAN consistof two networks trained in adversarial manner, i) a generator that adopts multi-layer feature fusion convolution network to enhance input noisy speech, and ii) a discriminator that attemptsto minimize the discrepancy between the distributions of the clean speech signal and enhanced speech signal. Experiment re-sults demonstrated superior performance of the proposed modelagainst several state-of-the-art