EMGSE: Acoustic/EMG Fusion for Multimodal Speech Enhancement

TL;DR

This paper introduces EMGSE, a multimodal speech enhancement system that combines audio and facial EMG signals, demonstrating improved performance especially in challenging noise conditions, with cheek EMG being sufficient for effective enhancement.

Contribution

The paper presents a novel EMGSE framework integrating audio and facial EMG signals for speech enhancement, highlighting the effectiveness of EMG data in challenging environments.

Findings

EMGSE outperforms audio-only systems in noisy conditions.

Facial EMG provides valuable articulatory information for speech enhancement.

Cheek EMG alone is sufficient for effective multimodal SE.

Abstract

Multimodal learning has been proven to be an effective method to improve speech enhancement (SE) performance, especially in challenging situations such as low signal-to-noise ratios, speech noise, or unseen noise types. In previous studies, several types of auxiliary data have been used to construct multimodal SE systems, such as lip images, electropalatography, or electromagnetic midsagittal articulography. In this paper, we propose a novel EMGSE framework for multimodal SE, which integrates audio and facial electromyography (EMG) signals. Facial EMG is a biological signal containing articulatory movement information, which can be measured in a non-invasive way. Experimental results show that the proposed EMGSE system can achieve better performance than the audio-only SE system. The benefits of fusing EMG signals with acoustic signals for SE are notable under challenging circumstances. Furthermore, this study reveals that cheek EMG is sufficient for SE.