Boosting Norwegian Automatic Speech Recognition
Javier de la Rosa Rolv-Arild Braaten Per Egil Kummervold Freddy Wetjen Svein Arne Brygfjeld
In this paper, we present several baselines for automatic speech recognition
(ASR) models for the two official written languages in Norway: Bokm{\aa}l and
Nynorsk. We compare the performance of models of varying sizes and pre-training
approaches on multiple Norwegian speech datasets. Additionally, we measure the
performance of these models against previous state-of-the-art ASR models, as
well as on out-of-domain datasets. We improve the state of the art on the
Norwegian Parliamentary Speech Corpus (NPSC) from a word error rate (WER) of
17.10\% to 7.60\%, with models achieving 5.81\% for Bokm{\aa}l and 11.54\% for
Nynorsk. We also discuss the challenges and potential solutions for further
improving ASR models for Norwegian.