BUT Systems for IWSLT 2023 Marathi - Hindi Low Resource Speech Translation Task

This paper describes the systems submitted for
Marathi to Hindi low-resource speech translation
task. Our primary submission is based on
an end-to-end direct speech translation system,
whereas the contrastive one is a cascaded system.
The backbone of both the systems is a
Hindi-Marathi bilingual ASR system trained
on 2790 hours of imperfect transcribed speech.
The end-to-end speech translation system was
directly initialized from the ASR, and then finetuned
for direct speech translation with an auxiliary
CTC loss for translation. The MT model
for the cascaded system is initialized from a
cross-lingual language model, which was then
fine-tuned using 1.6 M parallel sentences. All
our systems were trained from scratch on publicly
available datasets. In the end, we use a language
model to re-score the n-best hypotheses.
Our primary submission achieved 30.5 and 39.6
BLEU whereas the contrastive system obtained
21.7 and 28.6 BLEU on official dev and test
sets respectively. The paper also presents the
analysis on several experiments that were conducted
and outlines the strategies for improving
speech translation in low-resource scenarios.