Published November 2, 2019
| Version v1
Conference paper
Open
On Using SpecAugment for End-to-End Speech Translation
Authors/Creators
- 1. Human Language Technology and Pattern Recognition Group Computer Science Department, RWTH Aachen University, 52062 Aachen, Germany & AppTek, 52062 Aachen, Germany
- 2. Human Language Technology and Pattern Recognition Group, Computer Science Department, RWTH Aachen University, 52062 Aachen, Germany
Description
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.