  We present a novel multi-modal unspoken punctuation prediction system for the
English language which combines acoustic and text features. We demonstrate for
the first time, that by relying exclusively on synthetic data generated using a
prosody-aware text-to-speech system, we can outperform a model trained with
expensive human audio recordings on the unspoken punctuation prediction
problem. Our model architecture is well suited for on-device use. This is
achieved by leveraging hash-based embeddings of automatic speech recognition
text output in conjunction with acoustic features as input to a quasi-recurrent
neural network, keeping the model size small and latency low.
