Deep learning–empowered triboelectric acoustic textile for voice perception and intuitive generative AI-voice access on clothing
- Beibei Shao
- Tai-Chen Wu
- Zhi-Xian Yan
- Tien-Yu Ko
- Wei-Chen Peng
- Dun-Jie Jhan
- Yu-Hsiang Chang
- Jiun-Wei Fong
- Ming-Han Lu
- Wei-Chun Yang
- Jiann-Yeu Chen
- Ming-Yen Lu
- Baoquan Sun
- Heng-Jui Liu
- Ruiyuan Liu
- Ying-Chih Lai
2025-10-08
Integrating generative artificial intelligence (AI) chatbots with acoustic perception textiles allows everyday clothing to retrieve information, seek advice, and perform tasks through voice interactions. Here, we present a deep learning (DL)–empowered triboelectric AI acoustic textile (A-Textile) leveraging electrostatic charges on clothing for imperceptible, active voice perception and AI access. The multilayered A-Textile features a composite coating of three-dimensional SnS 2 nanoflowers (NFs) embedded in silicone rubber to enhance charge capture and transfer, along with a SnS 2 NFs–decorated graphite-like carbonized textile for charge accumulation and preservation. This design maximizes the charge density on the textile, achieving a 21-volt output, 1.2 volts per pascal sensitivity, 1-hertz resolution, and a wide sound response frequency range of 80 to 900 hertz. Using a well-trained DL model, the A-Textile precisely classifies and visualizes voice commands for internet-of-things control and cloud information access. Furthermore, we demonstrate its integration with ChatGPT, providing an intuitive interface for engaging with generative AI services to perform sophisticated tasks.