Affect Decoding in Phonated and Silent Speech Production from Surface EMG
Abstract
The expression of affect is integral to spoken communication, yet, its link to underlying articulatory execution remains unclear. Measures of articulatory muscle activity such as EMG could reveal how speech production is modulated by emotion alongside acoustic speech analyses. We investigate affect decoding from facial and neck surface electromyography (sEMG) during phonated and silent speech production. For this purpose, we introduce a dataset comprising 2,780 utterances from 12 participants across 3 tasks, on which we evaluate both intra- and inter-subject decoding using a range of features and model embeddings. Our results reveal that EMG representations reliably discriminate frustration with up to 0.845 AUC, and generalize well across articulation modes. Our ablation study further demonstrates that affective signatures are embedded in facial motor activity and persist in the absence of phonation, highlighting the potential of EMG sensing for affect-aware silent speech interfaces.
Keywords
Cite
@article{arxiv.2603.11715,
title = {Affect Decoding in Phonated and Silent Speech Production from Surface EMG},
author = {Simon Pistrosch and Kleanthis Avramidis and Zhao Ren and Tiantian Feng and Jihwan Lee and Monica Gonzalez-Machorro and Anton Batliner and Tanja Schultz and Shrikanth Narayanan and Björn W. Schuller},
journal= {arXiv preprint arXiv:2603.11715},
year = {2026}
}