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arXiv · 2308.06533

Knowledge Distilled Ensemble Model for sEMG-based Silent Speech Interface

Wenqiang Lai·Qihan Yang·Ye Mao·Endong Sun·Jiangnan Ye
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Abstract

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.

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Wenqiang Lai, Qihan Yang, Ye Mao, Endong Sun, Jiangnan Ye. 2023-08-07. Knowledge Distilled Ensemble Model for sEMG-based Silent Speech Interface. https://arxiv.org/abs/2308.06533

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