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modality:emg 6 pages 6 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

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.

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.