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

https://mdr.nims.go.jp/datasets/1b870ea2-3e17-4d58-9534-7b4ad9abb6f4

## File

- [Adv Elect Materials - 2026 - Nishioka - IonGating Reservoir Computing for Preprocessing Free Speech Recognition_のコピー.pdf](https://mdr.nims.go.jp/filesets/a4779bd3-4b19-49e9-b57f-526df349a743/download) ([Detail](https://mdr.nims.go.jp/filesets/a4779bd3-4b19-49e9-b57f-526df349a743.md))

## Id

1b870ea2-3e17-4d58-9534-7b4ad9abb6f4

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-10-16T05:28:51.975339Z

## Updated at

2026-10-19T00:30:03.930711Z

## Published at

2026-10-19T01:30:07.804479Z

## Doi



## First published url

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

## Date published

2026-04-26

## Recorded date published

2026-10

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Ion‐Gating Reservoir Computing for Preprocessing‐Free Speech Recognition
    from Throat Vibrations
  title_type: original
  lang: en

## Description

- description: '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. '
  description_type: abstract
  lang: und

## Creator

- name: Daiki Nishioka
  role: author
  orcid: https://orcid.org/0000-0002-3369-7700
  organization: National Institute for Materials Science
- name: Akito Tateyama
  role: author
  orcid: https://orcid.org/0000-0002-0403-3462
  organization: National Institute for Materials Science
- name: Hina Kitano
  role: author
  orcid: https://orcid.org/0009-0008-9132-0275
  organization: National Institute for Materials Science
- name: Takashi Nakanishi
  role: author
  orcid: https://orcid.org/0000-0002-8744-782X
  organization: National Institute for Materials Science
- name: Kazuya Terabe
  role: author
  orcid: https://orcid.org/0000-0003-3988-3456
  organization: National Institute for Materials Science
- name: Takashi Tsuchiya
  role: author
  orcid: https://orcid.org/0000-0002-6950-6160
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: Wiley

## Managing organization



## Keyword

- subject: reservoir computing
  schema: not_defined
- subject: ion-gating reservoir
  schema: not_defined
- subject: speech recognition
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/
  date_licensed: 2026-04-26

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Advanced Electronic Materials
  issn: 2199160X
  volume: '12'
  issue: '19'
  article_number: e00006

## Conference



## Related item



## Funding



## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: a4779bd3-4b19-49e9-b57f-526df349a743
  filename: Adv Elect Materials - 2026 - Nishioka - IonGating Reservoir Computing
    for Preprocessing Free Speech Recognition_のコピー.pdf
  content_type: application/pdf
  size: 2408572
  md5: 4077f88700ce27bbc2286c5f8f8f32e4

## Thumbnail

fileset_id: a4779bd3-4b19-49e9-b57f-526df349a743
filename: Adv Elect Materials - 2026 - Nishioka - IonGating Reservoir Computing for
  Preprocessing Free Speech Recognition_のコピー.pdf