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.
@article{arxiv.2307.01672,
title = {Boosting Norwegian Automatic Speech Recognition},
author = {Javier de la Rosa and Rolv-Arild Braaten and Per Egil Kummervold and Freddy Wetjen and Svein Arne Brygfjeld},
journal= {arXiv preprint arXiv:2307.01672},
year = {2023}
}
Comments
10 pages, 10 figures. Published as Proceedings NoDaLiDa 2023, pages 555--564