Boosting Norwegian Automatic Speech Recognition

Javier de la Rosa, Rolv-Arild Braaten, Per Egil Kummervold, Freddy Wetjen

Keywords: asr, norwegian, speech processing, automatic speech recognition, wav2vec, whisper

TL;DR: We found a good way to combine existing datasets to boost ASR for Norwegian

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.