EMGSE: Acoustic/EMG Fusion for Multimodal Speech Enhancement
This work addresses speech enhancement for applications in noisy environments, but it is incremental as it builds on existing multimodal approaches with a new data type.
The authors tackled speech enhancement in challenging conditions by integrating facial electromyography (EMG) signals with acoustic data, achieving better performance than audio-only systems, with notable benefits in low signal-to-noise ratios and unseen noise types.
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.