  Recently, SpecAugment, an augmentation scheme for automatic speech
recognition that acts directly on the spectrogram of input utterances, has
shown to be highly effective in enhancing the performance of end-to-end
networks on public datasets. In this paper, we demonstrate its effectiveness on
tasks with large scale datasets by investigating its application to the Google
Multidomain Dataset (Narayanan et al., 2018). We achieve improvement across all
test domains by mixing raw training data augmented with SpecAugment and
noise-perturbed training data when training the acoustic model. We also
introduce a modification of SpecAugment that adapts the time mask size and/or
multiplicity depending on the length of the utterance, which can potentially
benefit large scale tasks. By using adaptive masking, we are able to further
improve the performance of the Listen, Attend and Spell model on LibriSpeech to
2.2% WER on test-clean and 5.2% WER on test-other.
