Audio-visual Contrastive Alignment for Diffusion-based Visual-conditioned Speech Enhancement
arXiv:2606.23712v1 Announce Type: cross Abstract: 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 unclear. In this work, we propose to augment the diff
The proliferation of diffusion models and advancements in multi-modal AI are enabling novel approaches to traditional signal processing challenges like speech enhancement.
Improving speech enhancement technologies has broad applications across communication, accessibility, and human-computer interaction, making AI systems more robust in real-world conditions.
This work suggests a more explicit focus on cross-modal alignment in audio-visual diffusion models for speech enhancement can yield improved performance.
- · AI researchers
- · Speech technology developers
- · Call center industry
- · Hearing aid manufacturers
- · Legacy noise reduction software
Further research and development in multi-modal AI for real-time audio processing will be accelerated.
Enhanced clarity in noisy environments could improve the reliability and adoption of voice-controlled interfaces and remote collaboration tools.
More robust and natural human-AI interaction could emerge as AI systems become better at understanding speech in complex acoustic scenes.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI