2021/03/04 by Ha Nguyen, Ha-Thanh Nguyen, Yannick Estève +4
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Natural Language Processing Techniques #Speech Recognition and Synthesis #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.2103.03233
This paper has been accepted for presentation at IEEE ICASSP 2021
arxiv created 2021/03/04 · openalex publication_date 2021/03/04 · arxiv updated 2021/03/05 · openalex created_date 2021/03/15 · openalex updated_date 2026/07/28
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.