# Knowledge Distilled Ensemble Model for sEMG-based Silent Speech Interface

arXiv: https://arxiv.org/abs/2308.06533 · HTML: https://mlsanity.com/paper/2308.06533/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 7 Aug 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Rethinking Positional Encoding in Language Pre-training](https://mlsanity.com/paper/2006.15595/) (2020)
- [Silent Speech Interfaces for Speech Restoration: A Review](https://mlsanity.com/paper/2009.02110/) (2020)

## Built on by (most cited first, 1 of 1)

- [Scaling and Distilling Transformer Models for sEMG](https://mlsanity.com/paper/2507.22094/) (2025)

## Abstract (arXiv)

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.