An Empirical Study of End-To-End Simultaneous Speech Translation Decoding Strategies

Ha Nguyen; Yannick Estève; Laurent Besacier

ICASSP 2021accepted0 citations

An Empirical Study of End-To-End Simultaneous Speech Translation Decoding Strategies

Ha Nguyen, Yannick Estève, Laurent Besacier

Abstract

This paper proposes a decoding strategy for end-to-end simultaneous speech translation. We leverage end-to-end models trained in offline mode and conduct an empirical study for two language pairs (English-to-German and English-to-Portuguese). We also investigate different output token granularities including characters and Byte Pair Encoding (BPE) units. The results show that the proposed decoding approach allows to control BLEU/Average Lagging trade-off along different latency regimes. Our best decoding settings achieve comparable results with a strong cascade model evaluated on the simultaneous translation track of IWSLT 2020 shared task.

BibTeX

@inproceedings{icassp2021_anempiricalstudy,
  title = {An Empirical Study of End-To-End Simultaneous Speech Translation Decoding Strategies},
  author = {Ha Nguyen and Yannick Estève and Laurent Besacier},
  booktitle = {ICASSP 2021},
  year = {2021}
}