MIT Researchers Made AI That Can Capture Silent Speech

By using cuDNN-accelerated TensorFlow deep learning framework, the researchers trained their model on 31 hours of silently spoken text, which designed to identify subvocalized words from neuromuscular signals.

After that, the neural network tested on 15 people and achieved an accuracy level of 92%.