This work introduces a silent speech interface (SSI), proposing a few-layer graphene (FLG) strain sensing mechanism based on thorough cracks and AI-based self-adaptation capabilities that overcome the limitations of state-of-the-art technologies by simultaneously achieving high accuracy, high computational efficiency, and fast decoding speed while maintaining excellent user comfort. We demonstrate its application in a biocompatible textile-integrated ultrasensitive strain sensor embedded into a smart choker, which conforms to the user’s throat. Thanks to the structure of ordered through cracks in the graphene-coated textile, the proposed strain gauge achieves a gauge factor of 317 with <5% strain, corresponding to a 420% improvement over existing textile strain sensors fabricated by printing and coating technologies reported to date. Its high sensitivity allows it to capture subtle throat movements, simplifying signal processing and enabling the use of a computationally efficient neural network. The resulting neural network, based on a one-dimensional convolutional model, reduces computational load by 90% while maintaining a remarkable 95.25% accuracy in speech decoding. The synergy in sensor design and neural network optimization offers a promising solution for practical, wearable SSI systems, paving the way for seamless, natural silent communication in diverse settings.

这项工作引入了无声语音接口（SSI），提出了一种基于彻底裂纹和基于人工智能的自适应能力的几层石墨烯（FLG）应变传感机制，同时克服了最先进技术的局限性实现高精度、高计算效率和快速解码速度，同时保持出色的用户舒适度。我们展示了其在生物相容性纺织品集成超灵敏应变传感器中的应用，该传感器嵌入智能项圈中，符合用户的喉咙。由于石墨烯涂层织物中的有序贯通裂纹结构，所提出的应变计在 <5% 应变下实现了 317 的应变系数，相当于比据报告通过印刷和涂层技术制造的现有织物应变传感器提高了 420%日期。其高灵敏度使其能够捕捉微妙的喉咙运动，简化信号处理并允许使用计算效率高的神经网络。由此产生的神经网络基于一维卷积模型，可减少 90% 的计算负载，同时在语音解码方面保持高达 95.25% 的准确率。传感器设计和神经网络优化的协同作用为实用的可穿戴 SSI 系统提供了一种有前途的解决方案，为在不同环境中实现无缝、自然无声的通信铺平了道路。