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.

TAPS: Throat and Acoustic Paired Speech Dataset for Deep Learning-Based Speech Enhancement

In high-noise environments such as factories, subways, and busy streets, capturing clear speech is challenging due to background noise. Throat microphones provide a solution with their noise-suppressing properties, reducing the noise while recording speech. However, a significant limitation remains: high-frequency information is attenuated as sound waves pass through skin and tissue, reducing speech clarity. Recent deep learning approaches have shown promise in enhancing throat microphone recordings, but further progress is constrained by the absence of standardized dataset. We introduce a throat and acoustic paired speech dataset (TAPS), a collection of paired utterances recorded from 60 native Korean speakers using throat and acoustic microphones. To demonstrate the TAPS's utility, we tested three baseline deep learning models and identified the mapping-based approach as superior in improving speech quality and restoring content. Additionally, we propose an optimal method to mitigate the signal mismatch between throat and acoustic microphones, ensuring model performance. These results highlight the potential of TAPS to serve as a standardized dataset and advance research in throat microphone-based speech enhancement.