AI RESEARCH
emg2speech: Synthesizing speech from electromyography using self-supervised speech models
arXiv CS.CL
•
ArXi:2510.23969v2 Announce Type: replace-cross We present a neuromuscular speech interface that translates electromyographic (EMG) signals recorded from orofacial muscles during speech articulation directly into audio. We find that self-supervised speech (S3) representations are strongly linearly related to the electrical power of muscle activity: a simple linear mapping predicts EMG power from S3 representations with a correlation of r = 0.85. In addition, EMG power vectors associated with distinct articulatory gestures form structured, separable clusters.