This paper describes the ON-TRAC Consortium translation systems developed for
two challenge tracks featured in the Evaluation Campaign of IWSLT 2022:
low-resource and dialect speech translation. For the Tunisian Arabic-English
dataset (low-resource and dialect tracks), we build an end-to-end model as our
joint primary submission, and compare it against cascaded models that leverage
a large fine-tuned wav2vec 2.0 model for ASR. Our results show that in our
settings pipeline approaches are still very competitive, and that with the use
of transfer learning, they can outperform end-to-end models for speech
translation (ST). For the Tamasheq-French dataset (low-resource track) our
primary submission leverages intermediate representations from a wav2vec 2.0
model trained on 234 hours of Tamasheq audio, while our contrastive model uses
a French phonetic transcription of the Tamasheq audio as input in a Conformer
speech translation architecture jointly trained on automatic speech
recognition, ST and machine translation losses. Our results highlight that
self-supervised models trained on smaller sets of target data are more
effective to low-resource end-to-end ST fine-tuning, compared to large
off-the-shelf models. Results also illustrate that even approximate phonetic
transcriptions can improve ST scores.

本文介绍了 ON-TRAC 联盟为 2022 年 IWSLT 评测的两个挑战领域（低资源和方言演讲翻译）开发的翻译系统，该系统包括利用传递学习的管道方法和基于 wav2vec 2.0 模型转换的语音转换模型等。

IWSLT 2022 方言和低资源语音翻译任务的 ON-TRAC 联盟系统

ON-TRAC Consortium Systems for the IWSLT 2022 Dialect and Low-resource Speech Translation Tasks

This paper describes the ON-TRAC Consortium translation systems developed for
two challenge tracks featured in the Evaluation Campaign of IWSLT 2020, offline
speech translation and simultaneous speech translation. ON-TRAC Consortium is
composed of researchers from three French academic laboratories: LIA (Avignon
Universit\'e), LIG (Universit\'e Grenoble Alpes), and LIUM (Le Mans
Universit\'e). Attention-based encoder-decoder models, trained end-to-end, were
used for our submissions to the offline speech translation track. Our
contributions focused on data augmentation and ensembling of multiple models.
In the simultaneous speech translation track, we build on Transformer-based
wait-k models for the text-to-text subtask. For speech-to-text simultaneous
translation, we attach a wait-k MT system to a hybrid ASR system. We propose an
algorithm to control the latency of the ASR+MT cascade and achieve a good
latency-quality trade-off on both subtasks.

该研究论文讲述了 ON-TRAC Consortium 翻译系统在 2020 年 IWSLT 评估活动中开发的两个挑战赛轨道（离线语音翻译和同传语音翻译）的使用，主要使用基于注意力机制的编码器 - 解码器模型进行训练，该模型实现了数据增强和多模型集成等贡献，并探讨了一种控制 ASR+MT 级联延迟的算法，以实现良好的延迟 - 质量折衷的平衡。