Multi-modal Speech Enhancement with Limited Electromyography Channels

TL;DR

This paper introduces a multi-modal speech enhancement method using only 8-channel EMG signals combined with acoustic data, significantly improving speech quality and robustness in noisy environments compared to traditional approaches.

Contribution

The paper presents a novel EMG-based speech enhancement method utilizing only 8 channels and a modified SEMamba network with cross-modality modules, improving practicality and performance.

Findings

Achieves a PESQ gain of 0.235 in low SNR conditions

Demonstrates robustness in mismatched noise environments

Substantially outperforms traditional methods in noisy scenarios

Abstract

Speech enhancement (SE) aims to improve the clarity, intelligibility, and quality of speech signals for various speech enabled applications. However, air-conducted (AC) speech is highly susceptible to ambient noise, particularly in low signal-to-noise ratio (SNR) and non-stationary noise environments. Incorporating multi-modal information has shown promise in enhancing speech in such challenging scenarios. Electromyography (EMG) signals, which capture muscle activity during speech production, offer noise-resistant properties beneficial for SE in adverse conditions. Most previous EMG-based SE methods required 35 EMG channels, limiting their practicality. To address this, we propose a novel method that considers only 8-channel EMG signals with acoustic signals using a modified SEMamba network with added cross-modality modules. Our experiments demonstrate substantial improvements in speech quality and intelligibility over traditional approaches, especially in extremely low SNR settings. Notably, compared to the SE (AC) approach, our method achieves a significant PESQ gain of 0.235 under matched low SNR conditions and 0.527 under mismatched conditions, highlighting its robustness.