Many existing speech translation benchmarks focus on native-English speech in
high-quality recording conditions, which often do not match the conditions in
real-life use-cases. In this paper, we describe our speech translation system
for the multilingual track of IWSLT 2023, which focuses on the translation of
scientific conference talks. The test condition features accented input speech
and terminology-dense contents. The tasks requires translation into 10
languages of varying amounts of resources. In absence of training data from the
target domain, we use a retrieval-based approach (kNN-MT) for effective
adaptation (+0.8 BLEU for speech translation). We also use adapters to easily
integrate incremental training data from data augmentation, and show that it
matches the performance of re-training. We observe that cascaded systems are
more easily adaptable towards specific target domains, due to their separate
modules. Our cascaded speech system substantially outperforms its end-to-end
counterpart on scientific talk translation, although their performance remains
similar on TED talks.

本文介绍了一种针对具有口音的输入语音和术语密集的内容进行科学会议演讲翻译的语音翻译系统，使用了 kNN-MT 方法进行有效适应，该系统远优于其端对端对应物。