  The primary goal of this FBK's systems submission to the IWSLT 2022 offline
and simultaneous speech translation tasks is to reduce model training costs
without sacrificing translation quality. As such, we first question the need of
ASR pre-training, showing that it is not essential to achieve competitive
results. Second, we focus on data filtering, showing that a simple method that
looks at the ratio between source and target characters yields a quality
improvement of 1 BLEU. Third, we compare different methods to reduce the
detrimental effect of the audio segmentation mismatch between training data
manually segmented at sentence level and inference data that is automatically
segmented. Towards the same goal of training cost reduction, we participate in
the simultaneous task with the same model trained for offline ST. The
effectiveness of our lightweight training strategy is shown by the high score
obtained on the MuST-C en-de corpus (26.7 BLEU) and is confirmed in
high-resource data conditions by a 1.6 BLEU improvement on the IWSLT2020 test
set over last year's winning system.
