Single-channel speech enhancement models face significant performance degradation in extremely noisy environments. While prior work has shown that complementary bone-conducted speech can guide enhancement, effective integration of this noise-immune modality remains a challenge. This paper introduces a novel multimodal speech enhancement framework that integrates bone-conduction sensors with air-conducted microphones using a conditional diffusion model. Our proposed model significantly outperforms previously established multimodal techniques and a powerful diffusion-based single-modal baseline across a wide range of acoustic conditions.
@article{arxiv.2601.12354,
title = {Bone-conduction Guided Multimodal Speech Enhancement with Conditional Diffusion Models},
author = {Sina Khanagha and Bunlong Lay and Timo Gerkmann},
journal= {arXiv preprint arXiv:2601.12354},
year = {2026}
}