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 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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Geometry of orofacial neuromuscular signals: speech articulation decoding using surface electromyography
Each year, millions of individuals lose the ability to speak intelligibly due to causes such as neuromuscular disease, stroke, trauma, and head/neck cancer surgery (e.g. laryngectomy) or treatment (e.g. radiotherapy toxi…
Knowledge Distilled Ensemble Model for sEMG-based Silent Speech Interface
Voice disorders affect millions of people worldwide. Surface electromyography-based Silent Speech Interfaces (sEMG-based SSIs) have been explored as a potential solution for decades. However, previous works were limited …
ResEMGNet: A Lightweight Residual Deep Learning Architecture for Neuromuscular Disorder Detection from Raw EMG Signals
Amyotrophic Lateral Sclerosis (ALS) and Myopathy are debilitating neuromuscular disorders that demand accurate and efficient diagnostic approaches. In this study, we harness the power of deep learning techniques to detec…
DiagnosticElectromyography (EMG)Cross-Modal Masking for Robust Silent Speech Synthesis Using sEMG and Lipreading
Speech restoration through silent speech interfaces (SSIs) has emerged as a promising assistive technology for individuals with impaired or absent laryngeal voice production. Among non-invasive SSI modalities, surface el…
Speech SynthesisThe comparison of Higuchi fractal dimension and Sample Entropy analysis of sEMG: effects of muscle contraction intensity and TMS
The aim of the study was to examine how the complexity of surface electromyogram (sEMG) signal, estimated by Higuchi fractal dimension (HFD) and Sample Entropy (SampEn), change depending on muscle contraction intensity a…