arXiv · 2010.10203
Replacing Human Audio with Synthetic Audio for On-device Unspoken Punctuation Prediction
Abstract
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.
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Daria Soboleva, Ondrej Skopek, Márius Šajgalík, Victor Cărbune, Felix Weissenberger, Julia Proskurnia, Bogdan Prisacari, Daniel Valcarce, Justin Lu, Rohit Prabhavalkar, Balint Miklos. 2020-10-20. Replacing Human Audio with Synthetic Audio for On-device Unspoken Punctuation Prediction. https://arxiv.org/abs/2010.10203
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