Audio-Visual Speech Enhancement with Score-Based Generative Models

• URL: http://arxiv.org/abs/2306.01432v1
• Date: Fri, 2 Jun 2023 10:43:42 GMT
• Title: Audio-Visual Speech Enhancement with Score-Based Generative Models
• Authors: Julius Richter, Simone Frintrop, Timo Gerkmann
• Abstract summary: This paper introduces an audio-visual speech enhancement system that leverages score-based generative models. We exploit audio-visual embeddings obtained from a self-super-vised learning model that has been fine-tuned on lipreading. Experimental evaluations show that the proposed audio-visual speech enhancement system yields improved speech quality.
• Score: 22.559617939136505
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces an audio-visual speech enhancement system that leverages score-based generative models, also known as diffusion models, conditioned on visual information. In particular, we exploit audio-visual embeddings obtained from a self-super\-vised learning model that has been fine-tuned on lipreading. The layer-wise features of its transformer-based encoder are aggregated, time-aligned, and incorporated into the noise conditional score network. Experimental evaluations show that the proposed audio-visual speech enhancement system yields improved speech quality and reduces generative artifacts such as phonetic confusions with respect to the audio-only equivalent. The latter is supported by the word error rate of a downstream automatic speech recognition model, which decreases noticeably, especially at low input signal-to-noise ratios.

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