Affect Decoding in Phonated and Silent Speech Production from Surface EMG
This work addresses the challenge of understanding emotional expression in speech for applications like silent speech interfaces, though it is incremental in extending existing EMG methods to affect decoding.
The study tackled the problem of decoding affect from articulatory muscle activity during speech, finding that EMG representations could discriminate frustration with up to 0.845 AUC and generalize across phonated and silent speech modes.
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.