# Bone-conduction Guided Multimodal Speech Enhancement with Conditional Diffusion Models

arXiv: https://arxiv.org/abs/2601.12354 · HTML: https://mlsanity.com/paper/2601.12354/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Audio, speech and music, one of 14 directions in Diffusion models.
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 18 Jan 2026.

## Placed in

- Diffusion models / [Audio, speech and music](https://mlsanity.com/diffusion/audio-speech-and-music/)

## Builds on (most cited first, 8 of 8)

- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Speech Enhancement and Dereverberation with Diffusion-based Generative Models](https://mlsanity.com/paper/2208.05830/) (2022)
- [Conditional Diffusion Probabilistic Model for Speech Enhancement](https://mlsanity.com/paper/2202.05256/) (2022)
- [Analysing Diffusion-based Generative Approaches versus Discriminative Approaches for Speech Restoration](https://mlsanity.com/paper/2211.02397/) (2022)
- [Audio-Visual Speech Enhancement with Score-Based Generative Models](https://mlsanity.com/paper/2306.01432/) (2023)
- [Time-Domain Multi-modal Bone/air Conducted Speech Enhancement](https://mlsanity.com/paper/1911.09847/) (2019)
- [An Analysis of the Variance of Diffusion-based Speech Enhancement](https://mlsanity.com/paper/2402.00811/) (2024)

## Abstract (arXiv)

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.