Non-invasive electromyographic speech neuroprosthesis: a geometric perspective

ACL ARR 2026 January Submission2572 Authors

Keywords: Neuromotor speech interfaces, electromyographic (EMG), speech neuroprosthesis, accessible speech technology, brain-computer interfaces

Abstract: We present a neuromuscular speech interface that translates silently voiced articulations directly into text. We record surface electromyographic (EMG) signals from multiple articulatory sites on the face and neck as participants *silently* articulate speech, enabling direct EMG-to-text translation. Such an interface has the potential to restore communication for individuals who have lost the ability to produce intelligible speech due to laryngectomy, neuromuscular disease, stroke, or trauma-induced damage (e.g., radiotherapy toxicity) to the speech articulators. Prior work has largely focused on mapping EMG collected during *audible* articulation to time-aligned audio targets or transferring these targets to *silent* EMG recordings, which inherently requires audio and limits applicability to patients who can no longer speak. In contrast, we propose an efficient representation of high-dimensional EMG signals and demonstrate direct sequence-to-sequence EMG-to-text conversion at the phonemic level without relying on time-aligned audio.

Paper Type: Long

Research Area: Speech Processing and Spoken Language Understanding

Research Area Keywords: spoken language translation, corpus creation, benchmarking, language resources, multilingual corpora

Contribution Types: Approaches to low-resource settings, Approaches low compute settings-efficiency, Publicly available software and/or pre-trained models, Data resources

Languages Studied: English. EMG biosignal to text conversion.

Submission Number: 2572