  Multilingual pretraining for transfer learning significantly boosts the
robustness of low-resource monolingual ASR models. This study systematically
investigates three main aspects: (a) the impact of transfer learning on model
performance during initial training or fine-tuning, (b) the influence of
transfer learning across dataset domains and languages, and (c) the effect on
rare-word recognition compared to non-rare words. Our finding suggests that
RNNT-loss pretraining, followed by monolingual fine-tuning with Minimum Word
Error Rate (MinWER) loss, consistently reduces Word Error Rates (WER) across
languages like Italian and French. WER Reductions (WERR) reach 36.2% and 42.8%
compared to monolingual baselines for MLS and in-house datasets. Out-of-domain
pretraining leads to 28% higher WERR than in-domain pretraining. Both rare and
non-rare words benefit, with rare words showing greater improvements with
out-of-domain pretraining, and non-rare words with in-domain pretraining.
