vocl
vocl translates electromyographic (EMG) signals from facial and throat muscles directly into synthetic speech — a communication interface that requires no vocalization. the system captures the electrical patterns of silent and attempted speech, maps them to language, and outputs audible words in real time.
the problem
roughly 1.4 billion people globally have some form of speech or language impairment. existing assistive communication tools — gaze trackers, button grids, eye-scanning systems — are slow, exhausting, and far behind the pace of natural thought. they translate intent through a degraded physical channel. vocl takes a different approach: instead of routing around the body, it reads the intent before it fully becomes physical.
how it works
EMG sensors placed on the skin surface capture the electrical impulses produced by subvocal muscle movement — the micro-activations that occur even when you try to speak without making sound. a signal processing pipeline filters noise and segments the data into time windows. a neural network, trained on labeled EMG recordings, maps these patterns to phonemes and words. the output is passed through a text-to-speech engine to produce audible speech in near real time.
technical approach
the core model is a CNN-LSTM architecture suited for temporal EMG classification. training data spans both voiced and subvocalized speech attempts. the inference pipeline is optimized for low latency — the gap between thought and audible output needs to feel like speech, not like typing.
who it's for
ALS, laryngeal cancer, locked-in syndrome, severe dysarthria. anyone whose voice has been taken from them, or who was never given one. vocl is built to close that gap.