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基于TENG的自动驱动静音语音识别接口:从辅助通信到沉浸式AR/VR交互

Shuai Lin¹, Yanmin Guo¹, Xiangyao Zeng¹

¹School of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, People's Republic of China.

Nano-micro letters

|January 11, 2026

概括

这项研究引入了一种新的无声语音识别系统,使用灵活的压力传感器和深度学习. 它准确地将唇部运动解码为语音信号,以增强通信和非接触式设备控制.

科学领域:

生物医学工程生物医学工程
人与计算机的交互
材料科学材料科学材料科学

背景情况:

沉默的语言沟通对于言语障碍者来说是一个至关重要的领域.
准确的实时获取和解码部运动,以实现无声语音识别仍然具有挑战性.
现有的方法缺乏可靠无声语音解码所需的灵敏度和精度.

研究的目的:

开发一个实时无声语音识别系统.
集成基于 triboelectric 纳米发电机的灵活压力传感器 (FPS) 与深度学习框架.
通过解码静音语音来实现对设备的无接触控制.

主要方法:

在FPS中使用多孔金字塔结构的膜,用于敏感的低力压力检测.
开发了一个卷积神经网络-长期短期记忆 (CNN-LSTM) 混合网络用于信号解码.
集成了FPS和CNN-LSTM模型,用于实时静音语音获取和识别.

主要成果:

FPS在检测低力下巴运动时表现出高灵敏度 (1V N-1为0-10N和4.6V N-1为10-24N).
在CNN-LSTM模型实现了95.83%的分类准确度,对30个每日单词类别.
解码的无声语音信号被成功转化为可执行的智能手机命令.

结论:

拟议的系统为无声语音识别和通信提供了一个有希望的解决方案.
集成先进的传感器和深度学习可以实现精确的非接触式人机交互.
潜在的应用包括AR/VR环境和语音障碍患者的辅助技术.

关键词:

增强现实/虚拟现实互动互动深度学习是一种深度学习.灵活的压力传感器无声语音识别系统无声语音识别系统三元电纳米发电机.

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