  In this paper, we are comparing monolingual Wav2Vec 2.0 models with various
multilingual models to see whether we could improve speech recognition
performance on a unique oral history archive containing a lot of mixed-language
sentences. Our main goal is to push forward research on this unique dataset,
which is an extremely valuable part of our cultural heritage. Our results
suggest that monolingual speech recognition models are, in most cases, superior
to multilingual models, even when processing the oral history archive full of
mixed-language sentences from non-native speakers. We also performed the same
experiments on the public CommonVoice dataset to verify our results. We are
contributing to the research community by releasing our pre-trained models to
the public.
