VSEGAN: Visual Speech Enhancement Generative Adversarial Network
Xinmeng Xu, Yang Wang, Dongxiang Xu, Yiyuan Peng, Cong Zhang, Jie Jia, Binbin Chen
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 framework that involves visual information for speech enhancement, by incorporating a Generative Adversarial Network (GAN). In particular, the proposed visual speech enhancement GAN consists of 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 attempts to minimize the discrepancy between the distributions of the clean speech signal and enhanced speech signal. Experiment results demonstrated superior performance of the proposed model against several state-of-the-art models.