  Since facial actions such as lip movements contain significant information
about speech content, it is not surprising that audio-visual speech enhancement
methods are more accurate than their audio-only counterparts. Yet,
state-of-the-art approaches still struggle to generate clean, realistic speech
without noise artifacts and unnatural distortions in challenging acoustic
environments. In this paper, we propose a novel audio-visual speech enhancement
framework for high-fidelity telecommunications in AR/VR. Our approach leverages
audio-visual speech cues to generate the codes of a neural speech codec,
enabling efficient synthesis of clean, realistic speech from noisy signals.
Given the importance of speaker-specific cues in speech, we focus on developing
personalized models that work well for individual speakers. We demonstrate the
efficacy of our approach on a new audio-visual speech dataset collected in an
unconstrained, large vocabulary setting, as well as existing audio-visual
datasets, outperforming speech enhancement baselines on both quantitative
metrics and human evaluation studies. Please see the supplemental video for
qualitative results at
https://github.com/facebookresearch/facestar/releases/download/paper_materials/video.mp4.
