SOURCE-LINKED INTELLIGENCE
Soft Active Electromyography Interface for Machine Learning-Enabled Silent Speech Recognition
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 (F
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T12:36:09.000Z
First collected: 2026-09-21T08:32:02.028Z. This is not the publication date.