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

• URL: http://arxiv.org/abs/2604.18920v2
• Date: Mon, 27 Apr 2026 02:01:31 GMT
• Title: Comparison of sEMG Encoding Accuracy Across Speech Modes Using Articulatory and Phoneme Features
• Abstract summary: Speech Arttory Coding (SPARC) features can linearly predict surface electromy (sEMG) across electrode, mimed, and subvocal speech in twenty-four subjects.<n>Aloud and mimed speech perform comparably, and subvocal speech remains above chance, indicating detectable articulatory activity.<n>This study focuses on representation/encoding analysis (not end-to-end decoding) and supports modeling as a robust and interpretable intermediate target for sEMG-based silent-speech modeling.
• Score: 12.594831745032574
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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