Tu, Mei
Liu, Wei
Wang, Lijie
Chen, Xiao
Wen, Xue
2019-11-02
<p>This paper describes our end-to-end speech translation system for the speech translation task of lectures and TED talks from English to German for IWSLT Evaluation 2019. We propose layer-tied self-attention for end-to-end speech translation. Our method takes advantage of sharing weights of speech encoder and text decoder. The representation of source speech and the representation of target text are coordinated layer by layer, so that the speech and text can learn a better alignment during the training procedure. We also adopt data augmentation to enhance the parallel speech-text corpus. The En-De experimental results show that our best model achieves 17.68 on tst2015. Our ASR achieves WER of 6.6% on TED-LIUM test set. The En-Pt model can achieve about 11.83 on the MuST-C dev set.</p>
https://doi.org/10.5281/zenodo.3525548
oai:zenodo.org:3525548
eng
Zenodo
https://zenodo.org/communities/iwslt2019
https://doi.org/10.5281/zenodo.3525547
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
End-to-end Speech Translation System Description of LIT for IWSLT 2019
info:eu-repo/semantics/article