Knowledge Distilled Ensemble Model for sEMG-based Silent Speech Interface
This addresses voice disorders by enabling silent speech interfaces with expanded vocabulary, though it appears incremental as it builds on existing deep learning and knowledge distillation methods.
The paper tackled the problem of limited vocabulary and manual feature extraction in surface electromyography-based silent speech interfaces by proposing a lightweight deep learning knowledge-distilled ensemble model, achieving a test accuracy of 85.9% on a 26 NATO phonetic alphabets dataset.
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.