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Our research presents a wearable Silent Speech Interface (SSI) technologythat excels in device comfort, time-energy efficiency, and speech decodingaccuracy for real-world use. We developed a biocompatible, durable textilechoker with an embedded graphene-based strain sensor, capable of accuratelydetecting subtle throat movements. This sensor, surpassing other strain sensorsin sensitivity by 420%, simplifies signal processing compared to traditionalvoice recognition methods. Our system uses a computationally efficient neuralnetwork, specifically a one-dimensional convolutional neural network withresidual structures, to decode speech signals. This network is energy andtime-efficient, reducing computational load by 90% while achieving 95.25%accuracy for a 20-word lexicon and swiftly adapting to new users and words withminimal samples. This innovation demonstrates a practical, sensitive, andprecise wearable SSI suitable for daily communication applications.

Ultrasensitive Textile Strain Sensors Redefine Wearable Silent Speech Interfaces with High Machine Learning Efficiency