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arXiv · 1811.00707

Training Neural Speech Recognition Systems with Synthetic Speech Augmentation

Jason Li·Ravi Gadde·Boris Ginsburg·Vitaly Lavrukhin
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Abstract

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.

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BibTeXRIS

Jason Li, Ravi Gadde, Boris Ginsburg, Vitaly Lavrukhin. 2018-11-02. Training Neural Speech Recognition Systems with Synthetic Speech Augmentation. https://arxiv.org/abs/1811.00707

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