Comparison of sEMG Encoding Accuracy Across Speech Modes Using Articulatory and Phoneme Features
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 patter...
| Authors | Chenqian Le, Ruisi Li, Beatrice Fumagalli et al. |
|---|---|
| Primary category | cs.SD |
| Published | 20 April 2026 |
| Last updated at source | 27 April 2026 |
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