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An empirical study of end-to-end simultaneous speech translation decoding strategies
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| Ha Nguyen, Yannick Estève, Laurent Besacier |
| The International Conference on Acoustics, Speech, & Signal Processing (ICASSP), Toronto, Canada, 6-11 June, 2021 |
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
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