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説明:

Physical reservoir computing offers a promising route to efficient edge-AI, yet most reservoir-based speech-recognition demonstrations still rely on microphones and digital preprocessing. Here, we report a real-time spoken-digit recognition system that directly processes throat vibrations by integrating a π–gel-electret mechano-electric generator (MEG) sensor with a multi-channel ion-gel/graphene ion-gating reservoir (IGR). The MEG, a free-deformable electret-based vibration sensor, converts articulation-induced throat-surface vibrations into voltage signals, which are then transformed into pulse sequences and applied directly to the IGR gate without any spectral or handcrafted feature extraction. Channel-dependent Dirac-point shifts, nonlinear electric-double-layer dynamics, and distinct relaxation behaviors in the IGR expand the one-dimensional MEG output into high-dimensional reservoir states, which are decoded using a simple linear classifier. This hybrid MEG–IGR system achieves an impressive real-time spoken-digit recognition accuracy of 96.8%, demonstrating that ion-gating reservoirs can efficiently extract discriminative spatiotemporal features from raw biomechanical signals. By eliminating frequency-domain preprocessing and enabling compact, low-power front-end computation, the approach provides a viable pathway for on-device speech interfaces. Beyond speech recognition, direct throat-vibration sensing opens opportunities for robust human–machine interfaces, assistive communication technologies, and secure authentication systems based on spoof-resistant biomechanical signatures.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: reservoir computing, ion-gating reservoir, speech recognition

刊行年月日: 2026-04-26

出版者: Wiley

掲載誌:

• Advanced Electronic Materials (ISSN: 2199160X) vol. 12 issue. 19 e00006

研究助成金:

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1002/aelm.202600006

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更新時刻: 2026-10-19 09:30:03 +0900

MDRでの公開時刻: 2026-10-19 10:30:07 +0900