SPARC articulatory features predict sEMG signals more accurately than phoneme features across aloud, mimed, and subvocal speech, with consistent anatomical patterns and above-chance performance even in silent mode.
The mul- tivariate temporal response function (mtrf) toolbox: A matlab toolbox for relating neural signals to continuous stimuli
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Comparison of sEMG Encoding Accuracy Across Speech Modes Using Articulatory and Phoneme Features
SPARC articulatory features predict sEMG signals more accurately than phoneme features across aloud, mimed, and subvocal speech, with consistent anatomical patterns and above-chance performance even in silent mode.