  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.
