VSEGAN: Visual Speech Enhancement Generative Adversarial Network
Abstract
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
Keywords
Cite
@article{arxiv.2102.02599,
title = {VSEGAN: Visual Speech Enhancement Generative Adversarial Network},
author = {Xinmeng Xu and Yang Wang and Dongxiang Xu and Yiyuan Peng and Cong Zhang and Jie Jia and Binbin Chen},
journal= {arXiv preprint arXiv:2102.02599},
year = {2022}
}
Comments
Accepted by ICASSP 2022