Soft Active Electromyography Interface for Machine Learning-Enabled Silent Speech Recognition
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Computer Science > Machine Learning
arXiv:2608.27048 (cs)
[Submitted on 27 Aug 2026]
Title:Soft Active Electromyography Interface for Machine Learning-Enabled Silent Speech Recognition
Authors:Yuta Kurotaki, Shusuke Yamakoshi, Reitaro Yoshida, Yutaka Isoda, Tamami Takano, Yuji Isano, Yusuke Miyake, Kentaro Kuribayashi, Hiroki Ota
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Abstract:Silent speech recognition (SSR) provides an alternative communication pathway in the absence of audible speech. However, conventional approaches are limited by the need for constant facial attachment, privacy concerns, and unstable signal acquisition. Here, we propose a soft, active electromyography (EMG) interface that enables word-level SSR using machine learning. Worn on the hand, the device uses a fingertip electrode that can be positioned near the lips to acquire EMG signals only when needed. The interface integrates liquid metal (LM) interconnects, transparent flexible printed circuit (FPC) electrodes, and elastomer encapsulation to ensure high mechanical stability during finger motion. A deep neural network trained on these stable signals achieved a mean accuracy of 97.2 $\pm$ 1.3% across three subjects in classifying a 30-word vocabulary, demonstrating robust linguistic discrimination. Furthermore, real-time drone control validates the practicality of this approach in noisy and privacy-sensitive environments where conventional voice recognition fails. This study highlights the potential of soft, wearable EMG systems as secure and intuitive human-machine interfaces.
| Comments: | 17 pages, 5 figures, supplementary information |
| Subjects: | Machine Learning (cs.LG); Human-Computer Interaction (cs.HC); Sound (cs.SD) |
| Cite as: | arXiv:2608.27048 [cs.LG] |
| (or arXiv:2608.27048v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.27048
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | Advanced Intelligent Systems 2026, 0, e70440 |
| Related DOI: | https://doi.org/10.1002/aisy.70440
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