Strategies for improving low resource speech to text translation relying
  on pre-trained ASR models
Santosh Kesiraju Marek Sarvas Tomas Pavlicek Cecile Macaire Alejandro Ciuba
This paper presents techniques and findings for improving the performance of
low-resource speech to text translation (ST). We conducted experiments on both
simulated and real-low resource setups, on language pairs English - Portuguese,
and Tamasheq - French respectively. Using the encoder-decoder framework for ST,
our results show that a multilingual automatic speech recognition system acts
as a good initialization under low-resource scenarios. Furthermore, using the
CTC as an additional objective for translation during training and decoding
helps to reorder the internal representations and improves the final
translation. Through our experiments, we try to identify various factors
(initializations, objectives, and hyper-parameters) that contribute the most
for improvements in low-resource setups. With only 300 hours of pre-training
data, our model achieved 7.3 BLEU score on Tamasheq - French data,
outperforming prior published works from IWSLT 2022 by 1.6 points.