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This study introduces a soft, active electromyography (EMG) interface designed for word-level silent speech recognition (SSR), addressing limitations of traditional methods that require constant facial attachment and raise privacy concerns. By utilizing a fingertip electrode positioned near the lips, the device captures stable EMG signals during hand movements, achieving a remarkable mean accuracy of 97.2% in classifying a 30-word vocabulary through a deep neural network. Additionally, the interface's effectiveness is demonstrated in real-time drone control, showcasing its potential in noisy and privacy-sensitive environments where conventional voice recognition falls short.
Achieving 97.2% accuracy in silent speech recognition, this soft EMG interface redefines how we can communicate silently and securely in everyday environments.
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 ± 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.