Non-invasive electromyographic speech neuroprosthesis: a geometric perspective

TL;DR

This paper introduces a non-invasive EMG-based speech interface that directly converts silent speech articulations into text, aiming to restore communication for speech-impaired individuals.

Contribution

It presents a novel geometric approach for direct EMG-to-text translation at the phonemic level without using time-aligned audio data.

Findings

Achieved direct sequence-to-sequence EMG-to-text conversion.

Demonstrated effectiveness on silent speech articulation.

Potential to aid speech-impaired communication restoration.

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.