Comparison of sEMG Encoding Accuracy Across Speech Modes Using Articulatory and Phoneme Features

Chenqian Le; Ruisi Li; Beatrice Fumagalli; Yasamin Esmaeili; Xupeng Chen; Amirhossein Khalilian-Gourtani; Tianyu He; Adeen Flinker; Yao Wang

arXiv · 2604.18920

Comparison of sEMG Encoding Accuracy Across Speech Modes Using Articulatory and Phoneme Features

Chenqian Le·Ruisi Li·Beatrice Fumagalli·Yasamin Esmaeili·Xupeng Chen·Amirhossein Khalilian-Gourtani·Tianyu He·Adeen Flinker·Yao Wang

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Abstract

We test whether Speech Articulatory Coding (SPARC) features can linearly predict surface electromyography (sEMG) envelopes across aloud, mimed, and subvocal speech in twenty-four subjects. Using elastic-net multivariate temporal response function (mTRF) with sentence-level cross-validation, SPARC yields higher prediction accuracy than phoneme one-hot representations on nearly all electrodes and in all speech modes. Aloud and mimed speech perform comparably, and subvocal speech remains above chance, indicating detectable articulatory activity. Variance partitioning shows a substantial unique contribution from SPARC and a minimal unique contribution from phoneme features. mTRF weight patterns reveal anatomically interpretable relationships between electrode sites and articulatory movements that remain consistent across modes. This study focuses on representation/encoding analysis (not end-to-end decoding) and supports SPARC as a robust and interpretable intermediate target for sEMG-based silent-speech modeling.

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Chenqian Le, Ruisi Li, Beatrice Fumagalli, Yasamin Esmaeili, Xupeng Chen, Amirhossein Khalilian-Gourtani, Tianyu He, Adeen Flinker, Yao Wang. 2026-04-27. Comparison of sEMG Encoding Accuracy Across Speech Modes Using Articulatory and Phoneme Features. https://arxiv.org/abs/2604.18920

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