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Building an accurate automatic speech recognition (ASR) system requires alarge dataset that contains many hours of labeled speech samples produced by adiverse set of speakers. The lack of such open free datasets is one of the mainissues preventing advancements in ASR research. To address this problem, wepropose to augment a natural speech dataset with synthetic speech. We trainvery large end-to-end neural speech recognition models using the LibriSpeechdataset augmented with synthetic speech. These new models achieve state of theart Word Error Rate (WER) for character-level based models without an externallanguage model.

Training Neural Speech Recognition Systems with Synthetic Speech Augmentation