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Evidence record 910 · automatically gathered

Audio-visual Contrastive Alignment for Diffusion-based Visual-conditioned Speech Enhancement

Audio-visual speech enhancement (AVSE) exploits visual cues such as lip movements to recover speech in noisy environments. Recent work introduced diffusion-based unsupervised AVSE, where a speech diffusion model conditioned on visual features via cross-attention is trained and used as a data-driven prior for posterior sampling-based speech enhancement. Despite promising performance over its audio-only counterpart, the impact of explicitly enforcing cross-modal alignment in the fusion remains unc

Record details

Published: 16 June 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Environment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (16 June 2026), “Audio-visual Contrastive Alignment for Diffusion-based Visual-conditioned Speech Enhancement,” evidence record 910, https://ethics.ai/record/910 (originally published by arXiv).

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