  We present SpecAugment, a simple data augmentation method for speech
recognition. SpecAugment is applied directly to the feature inputs of a neural
network (i.e., filter bank coefficients). The augmentation policy consists of
warping the features, masking blocks of frequency channels, and masking blocks
of time steps. We apply SpecAugment on Listen, Attend and Spell networks for
end-to-end speech recognition tasks. We achieve state-of-the-art performance on
the LibriSpeech 960h and Swichboard 300h tasks, outperforming all prior work.
On LibriSpeech, we achieve 6.8% WER on test-other without the use of a language
model, and 5.8% WER with shallow fusion with a language model. This compares to
the previous state-of-the-art hybrid system of 7.5% WER. For Switchboard, we
achieve 7.2%/14.6% on the Switchboard/CallHome portion of the Hub5'00 test set
without the use of a language model, and 6.8%/14.1% with shallow fusion, which
compares to the previous state-of-the-art hybrid system at 8.3%/17.3% WER.
