  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
