Ion‐Gating Reservoir Computing for Preprocessing‐Free Speech Recognition from Throat Vibrations

ABSTRACT 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.