Boosting Norwegian Automatic Speech Recognition

Javier de la Rosa, Rolv-Arild Braaten, Per Egil Kummervold, Freddy Wetjen, Svein Arne Brygfjeld
7/4/2023
cs.CL

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Abstract

In this paper, we present several baselines for automatic speech recognition (ASR) models for the two official written languages in Norway: Bokmål 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 Bokmål and 11.54\% for Nynorsk. We also discuss the challenges and potential solutions for further improving ASR models for Norwegian.

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Cite this paper

@article{rosa2023boosting,
  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},
  year   = {2023},
  eprint = {2307.01672},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.01672v1}
}

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