Dr. Naoki Kimura

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modality:emg 22 pages 22 reviewed 0 imported

EMG

This page groups the current SSI review database by the real `modality:` tag `modality:emg`.

The list below includes every paper page that currently carries this technique label.

Papers

reviewedarXiv2026

Multi-Subject Pretraining Enables Short-Calibration Personalization for Closed-Corpus Surface EMG Speech Decoding

Chenqian Le, Beatrice Fumagalli, Yasamin Esmaeili, Xupeng Chen, Tianyu He, Nikasadat Emami, Adeen Flinker, Yao Wang

標準化8ch顔／頸部sEMG・閉じた50文・27人LOSOで、多被験者事前学習＋対象微調整は21.7% CER / 31.9% WER。3分キャリブレーションは約13分と有意差なし（20.5%/31.7%）。未見文では78.6% CERまで崩壊。開語彙や臨床完成ではない。

reviewedarXiv2026

Cross-Modal Masking for Robust Silent Speech Synthesis Using sEMG and Lipreading

Eder del Blanco, David Gimeno-Gómez, Eva Navas, Carlos-D Martínez-Hinarejos, Inma Hernáez

The paper advances silent speech synthesis by leveraging masked training to robustly fuse electromyography and lipreading, showing improved performance and resilience, but adaptation to laryngectomized users remains challenging.

reviewedarXiv2026

A 1000-hour EEG-EMG-audio dataset of Japanese speech production

Motoshige Sato, Ilya Horiguchi, Masakazu Inoue, Kenichi Tomeoka, Eri Hatakeyama, Yuya Kita, Atsushi Yamamoto, Ippei Fujisawa, Shuntaro Sasai

A 1020-hour multimodal EEG-EMG-audio dataset for Japanese overt speech vastly expands data resources, enabling diverse speech decoding and EEG research, though generalization is limited by three participants and no decoding benchmarks are presented.

reviewedarXiv2026

Affect Decoding in Phonated and Silent Speech Production from Surface EMG

Simon Pistrosch, Kleanthis Avramidis, Zhao Ren, Tiantian Feng, Jihwan Lee, Monica Gonzalez-Machorro, A. Batliner, Tanja Schultz, Shrikanth Narayanan, Björn W. Schuller

Useful evidence that prompted silent articulation carries affect cues, with silent-only AUC 0.829 within a person; weak unseen-speaker transfer and inseparable facial-expression effects limit deployment claims.

reviewedarXiv2026

SilentWear: an Ultra-Low Power Wearable System for EMG-based Silent Speech Recognition

Giusy Spacone, Sebastian Frey, Giovanni Pollo, Alessio Burrello, Daniele Jahier Pagliari, Victor Kartsch, Andrea Cossettini, Luca Benini

A useful dry-neckband and embedded-CNN study: silent balanced accuracy falls from 77.5% across pooled-day batches to 59.3% on a new day; 2.47 ms is compute time, while closed-loop usability remains untested.

reviewedarXiv2026

EMG-to-Speech with Fewer Channels

Injune Hwang, Jaejun Lee, Kyogu Lee

Exhaustive subset search shows useful channel complementarity, and full-channel pretraining helps smaller EMG inputs. Single-person evaluation, unclear selection independence and a dropout text/figure conflict limit layout recommendations.

reviewedarXiv2025

CAT-Net: A Cross-Attention Tone Network for Cross-Subject EEG-EMG Fusion Tone Decoding

Yifan Zhuang, Calvin Huang, Zepeng Yu, Yongjie Zou, Jiawei Ju

脳波と筋電を組み合わせ、声を出さずに発音した中国語の四声を分類する研究。学習に含まない人で平均85.10%を報告するが、文章認識ではなく、指標名や分割・チャネル選択手順には確認が必要。

reviewedarXiv2025

From Silent Signals to Natural Language: A Dual-Stage Transformer-LLM Approach

Nithyashree Sivasubramaniam

筋電から合成した音声の文字起こしをTransformerとGPT-2で修正し、約100発話で単語誤り率36%→30%を報告する。音声そのものの聞き取りやすさや日常利用の実証ではなく、学習・分割・意味保持の検証は不足している。

reviewedarXiv2025

Confidence-Based Self-Training for EMG-to-Speech: Leveraging Synthetic EMG for Robust Modeling

Xiaodan Chen, Xiaoxue Gao, Mathias Quoy, Alexandre Pitti, Nancy F. Chen

音声から作った合成筋電を選別して実データと混ぜ、発声時の実筋電で単語誤り率23.30%に対し21.87%を報告する。実筋電は1人で、1532人は合成元の音声話者。無声発話への有効性は未実証で、図と本文の不一致も残る。

reviewedarXiv / imported corpus page2023

Knowledge Distilled Ensemble Model for sEMG-based Silent Speech Interface

Wenqiang Lai, Qihan Yang, Mao Ye, Endong Sun, Jiangnan Ye

This paper delivers a practical spelling-focused sEMG silent speech system by compressing a ResNet ensemble into a lightweight model achieving 85.9% accuracy on the NATO alphabet with portable hardware, but remains limited to 5 young male subjects and speaker-dependent scenarios.

reviewedarXiv / imported corpus page2022

Sequence-to-Sequence Voice Reconstruction for Silent Speech in a Tonal Language

Huiyan Li, Haohong Lin, You Wang, Hengyang Wang, Ming Zhang, Han Gao, Qing Ai, Zhiyuan Luo, Guang Li

SSRNet innovatively applies duration-aware Seq2Seq modeling and tonal multitask learning to reconstruct intelligible Mandarin speech from facial sEMG signals, markedly improving performance over prior methods but remains speaker-dependent with limited deployment evaluation.

reviewedarXiv / imported corpus page2021

An Improved Model for Voicing Silent Speech

David Gaddy, Dan Klein

This paper substantially improves open-vocabulary silent speech voicing using learned convolutional EMG features, Transformer modeling, and phoneme supervision, reducing WER from 68.0% to 42.2% automatic and 32.3% human in a single-speaker lab setting.