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arXiv · 2203.17263

Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis

Karren Yang·Dejan Markovic·Steven Krenn·Vasu Agrawal·Alexander Richard
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Abstract

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.

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Karren Yang, Dejan Markovic, Steven Krenn, Vasu Agrawal, Alexander Richard. 2022-03-31. Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis. https://arxiv.org/abs/2203.17263

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