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arXiv · 2307.01672

Boosting Norwegian Automatic Speech Recognition

Javier de la Rosa·Rolv-Arild Braaten·Per Egil Kummervold·Freddy Wetjen·Svein Arne Brygfjeld
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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: 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.

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Javier de la Rosa, Rolv-Arild Braaten, Per Egil Kummervold, Freddy Wetjen, Svein Arne Brygfjeld. 2023-07-04. Boosting Norwegian Automatic Speech Recognition. https://arxiv.org/abs/2307.01672

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