Boosting Norwegian Automatic Speech Recognition

• URL: http://arxiv.org/abs/2307.01672v1
• Date: Tue, 4 Jul 2023 12:05:15 GMT
• Title: Boosting Norwegian Automatic Speech Recognition
• Authors: Javier de la Rosa, Rolv-Arild Braaten, Per Egil Kummervold, Freddy Wetjen, Svein Arne Brygfjeld
• Abstract summary: We present several baselines for automatic speech recognition (ASR) models for the two official written languages in Norway: Bokmaal and Nynorsk. We compare the performance of models of varying sizes and pre-training approaches on multiple Norwegian speech 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 Bokmaal and 11.54% for Nynorsk.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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