Voice disorders affect millions of people worldwide. Surface electromyography-based Silent Speech Interfaces (sEMG-based SSIs) have been explored as a potential solution for decades. However, previous works were limited by small vocabularies and manually extracted features from raw data. To address these limitations, we propose a lightweight deep learning knowledge-distilled ensemble model for sEMG-based SSI (KDE-SSI). Our model can classify a 26 NATO phonetic alphabets dataset with 3900 data samples, enabling the unambiguous generation of any English word through spelling. Extensive experiments validate the effectiveness of KDE-SSI, achieving a test accuracy of 85.9\%. Our findings also shed light on an end-to-end system for portable, practical equipment.
Cite
@article{arxiv.2308.06533,
title = {Knowledge Distilled Ensemble Model for sEMG-based Silent Speech Interface},
author = {Wenqiang Lai and Qihan Yang and Ye Mao and Endong Sun and Jiangnan Ye},
journal= {arXiv preprint arXiv:2308.06533},
year = {2023}
}