  Building an accurate automatic speech recognition (ASR) system requires a
large dataset that contains many hours of labeled speech samples produced by a
diverse set of speakers. The lack of such open free datasets is one of the main
issues preventing advancements in ASR research. To address this problem, we
propose to augment a natural speech dataset with synthetic speech. We train
very large end-to-end neural speech recognition models using the LibriSpeech
dataset augmented with synthetic speech. These new models achieve state of the
art Word Error Rate (WER) for character-level based models without an external
language model.
