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