Conference paper Open Access
On Using SpecAugment for End-to-End Speech Translation
Bahar, Parnia; Zeyer, Albert; Schlüter, Ralf; Ney, Hermann
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.
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- Publication date:
- November 2, 2019
- DOI:
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- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International