Yao Chuanjie, Liu Suhang, Liu Zhengjie, Huang Shuang, Sun Tiancheng, He Mengyi, Xiao Gemin, Ouyang Han, Tao Yu, Qiao Yancong, Li Mingqiang, Li Zhou, Shi Peng, Chen Hui-Jiuan, Xie Xi
State Key Laboratory of Optoelectronic Materials and Technologies, School of Electronics and Information Technology, Sun Yat-Sen University, Guangzhou, China.
Guangdong Province Key Laboratory of Display Material and Technology, Sun Yat-Sen University, Guangzhou, China.
Nat Commun. 2025 May 8;16(1):4276. doi: 10.1038/s41467-025-59523-6.
Human-machine voice interaction based on speech recognition offers an intuitive, efficient, and user-friendly interface, attracting wide attention in applications such as health monitoring, post-disaster rescue, and intelligent control. However, conventional microphone-based systems remain challenging for complex human-machine collaboration in noisy environments. Herein, an anti-noise triboelectric acoustic sensor (Anti-noise TEAS) based on flexible nanopillar structures is developed and integrated with a convolutional neural network-based deep learning model (Anti-noise TEAS-DLM). This highly synergistic system enables robust acoustic signal recognition for human-machine collaboration in complex, noisy scenarios. The Anti-noise TEAS directly captures acoustic fundamental frequency signals from laryngeal mixed-mode vibrations through contact sensing, while effectively suppressing environmental noise by optimizing device-structure buffering. The acoustic signals are subsequently processed and semantically decoded by the DLM, ensuring high-fidelity interpretation. Evaluated in both simulated virtual and real-life noisy environments, the Anti-noise TEAS-DLM demonstrates near-perfect noise immunity and reliably transmits various voice commands to guide robotic systems in executing complex post-disaster rescue tasks with high precision. The combined anti-noise robustness and execution accuracy endow this DLM-enhanced Anti-noise TEAS as a highly promising platform for next-generation human-machine collaborative systems operating in challenging noisy environments.
基于语音识别的人机语音交互提供了直观、高效且用户友好的界面，在健康监测、灾后救援和智能控制等应用中受到广泛关注。然而，传统的基于麦克风的系统在嘈杂环境中的复杂人机协作方面仍然面临挑战。在此，开发了一种基于柔性纳米柱结构的抗噪声摩擦电声学传感器（Anti-noise TEAS），并将其与基于卷积神经网络的深度学习模型（Anti-noise TEAS-DLM）集成。这种高度协同的系统能够在复杂、嘈杂的场景中实现强大的声学信号识别，用于人机协作。Anti-noise TEAS通过接触传感直接从喉部混合模式振动中捕获声学基频信号，同时通过优化器件结构缓冲有效抑制环境噪声。随后，声学信号由DLM进行处理和语义解码，确保高保真解释。在模拟虚拟和现实生活中的嘈杂环境中进行评估时，Anti-noise TEAS-DLM展示了近乎完美的抗噪声能力，并可靠地传输各种语音命令，以高精度指导机器人系统执行复杂的灾后救援任务。抗噪声鲁棒性和执行准确性的结合使这种DLM增强的Anti-noise TEAS成为在具有挑战性的嘈杂环境中运行的下一代人机协作系统的极具潜力的平台。
