Modality-Specific Speech Enhancement and Noise-Adaptive Fusion for Acoustic and Body-Conduction Microphone Framework

Body\-conduction microphone signals (BMS) bypass airborne sound, providing strong noise resistance. However, a complementary modality is required to compensate for the inherent loss of high\-frequency information. In this study, we propose a novel multi\-modal framework that combines BMS and acoustic microphone signals (AMS) to achieve both noise suppression and high\-frequency reconstruction. Unlike conventional multi\-modal approaches that simply merge features, our method employs two specialized networks\: a mapping-based model to enhance BMS and a masking-based model to denoise AMS. These networks are integrated through a dynamic fusion mechanism that adapts to local noise conditions, ensuring the optimal use of each modality's strengths. We performed evaluations on the TAPS dataset, augmented with DNS\-2023 noise clips, using objective speech quality metrics. The results clearly demonstrate that our approach outperforms single\-modal solutions in a wide range of noisy environments.