  This work investigates a simple data augmentation technique, SpecAugment, for
end-to-end speech translation. SpecAugment is a low-cost implementation method
applied directly to the audio input features and it consists of masking blocks
of frequency channels, and/or time steps. We apply SpecAugment on end-to-end
speech translation tasks and achieve up to +2.2\% \BLEU on LibriSpeech
Audiobooks En->Fr and +1.2% on IWSLT TED-talks En->De by alleviating
overfitting to some extent. We also examine the effectiveness of the method in
a variety of data scenarios and show that the method also leads to significant
improvements in various data conditions irrespective of the amount of training
data.
