# EMGSE: Acoustic/EMG Fusion for Multimodal Speech Enhancement

arXiv: https://arxiv.org/abs/2202.06507 · HTML: https://mlsanity.com/paper/2202.06507/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 14 Feb 2022.

## Builds on (most cited first, 10 of 10)

- [MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement](https://mlsanity.com/paper/1905.04874/) (2019)
- [Raw Waveform-based Speech Enhancement by Fully Convolutional Networks](https://mlsanity.com/paper/1703.02205/) (2017)
- [Improving noise robust automatic speech recognition with single-channel time-domain enhancement network](https://mlsanity.com/paper/2003.03998/) (2020)
- [WaveCRN: An Efficient Convolutional Recurrent Neural Network for End-to-end Speech Enhancement](https://mlsanity.com/paper/2004.04098/) (2020)
- [Improved Lite Audio-Visual Speech Enhancement](https://mlsanity.com/paper/2008.13222/) (2020)
- [Dual Application of Speech Enhancement for Automatic Speech Recognition](https://mlsanity.com/paper/2011.03840/) (2020)
- [Time-Domain Multi-modal Bone/air Conducted Speech Enhancement](https://mlsanity.com/paper/1911.09847/) (2019)
- [Robust Speaker Recognition Using Speech Enhancement And Attention Model](https://mlsanity.com/paper/2001.05031/) (2020)
- [A Study of Incorporating Articulatory Movement Information in Speech Enhancement](https://mlsanity.com/paper/2011.01691/) (2020)
- [Attention-based multi-task learning for speech-enhancement and speaker-identification in multi-speaker dialogue scenario](https://mlsanity.com/paper/2101.02550/) (2021)

## Abstract (arXiv)

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.