AI RESEARCH
Comparison of sEMG Encoding Accuracy Across Speech Modes Using Articulatory and Phoneme Features
arXiv CS.CL
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ArXi:2604.18920v1 Announce Type: cross 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.