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An Empirical Study of End-to-end Simultaneous Speech Translation Decoding Strategies

arXiv 2103.03233 ↗First posted 4 Mar 2021cs.CLcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 4 Mar 2021.

Builds on

14 of 25 references.

References · 14

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  4. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,355
  5. 2018How2: A Large-scale Dataset for Multimodal Language Understandingcited by 137
  6. 2019Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debatescited by 114
  7. 2019Monotonic Infinite Lookback Attention for Simultaneous Machine Translationcited by 103
  8. 2020SimulEval: An Evaluation Toolkit for Simultaneous Translationcited by 94
  9. 2019Monotonic Multihead Attentioncited by 91
  10. 2020Efficient Wait-k Models for Simultaneous Machine Translationcited by 59
  11. 2018Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translationcited by 51
  12. 2018Low-Latency Neural Speech Translationcited by 35
2 more
  1. 2020ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at IWSLT 2020cited by 9
  2. 2019ON-TRAC Consortium End-to-End Speech Translation Systems for the IWSLT 2019 Shared Taskcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 3; 2024: 5; 2025: 0; 2026: 1.

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  1. 2023AlignAtt: Using Attention-based Audio-Translation Alignments as a Guide for Simultaneous Speech Translationcited by 35
  2. 2022Attention as a Guide for Simultaneous Speech Translationcited by 32
  3. 2022Does Simultaneous Speech Translation need Simultaneous Models?cited by 22
  4. 2023End-to-End Simultaneous Speech Translation with Differentiable Segmentationcited by 22
  5. 2024StreamAtt: Direct Streaming Speech-to-Text Translation with Attention-based Audio History Selectioncited by 19
  6. 2024How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?cited by 17
  7. 2024Towards Achieving Human Parity on End-to-end Simultaneous Speech Translation via LLM Agentcited by 14
  8. 2021Simultaneous Speech Translation for Live Subtitling: from Delay to Displaycited by 8
  9. 2023Direct Models for Simultaneous Translation and Automatic Subtitling: FBK@IWSLT2023cited by 6
  10. 2024Recent Advances in End-to-End Simultaneous Speech Translationcited by 5
  11. 2021Impact of Encoding and Segmentation Strategies on End-to-End Simultaneous Speech Translationcited by 4
  12. 2021Visualization: the missing factor in Simultaneous Speech Translationcited by 2
2 more
  1. 2024NAIST Simultaneous Speech Translation System for IWSLT 2024cited by 1
  2. 2026Attention-Based Adaptive Policies for Simultaneous Speech-to-Text Translationcited by 0