  This paper presents NAVER LABS Europe's systems for Tamasheq-French and
Quechua-Spanish speech translation in the IWSLT 2023 Low-Resource track. Our
work attempts to maximize translation quality in low-resource settings using
multilingual parameter-efficient solutions that leverage strong pre-trained
models. Our primary submission for Tamasheq outperforms the previous state of
the art by 7.5 BLEU points on the IWSLT 2022 test set, and achieves 23.6 BLEU
on this year's test set, outperforming the second best participant by 7.7
points. For Quechua, we also rank first and achieve 17.7 BLEU, despite having
only two hours of translation data. Finally, we show that our proposed
multilingual architecture is also competitive for high-resource languages,
outperforming the best unconstrained submission to the IWSLT 2021 Multilingual
track, despite using much less training data and compute.
