Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Training Neural Speech Recognition Systems with Synthetic Speech
  Augmentation   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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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
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The categories of this paper are: math
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: alg-geom