Boosting Norwegian Automatic Speech Recognition
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Abstract (arXiv)
In this paper, 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. 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 Bokmaal and 11.54% for Nynorsk. We also discuss the challenges and potential solutions for further improving ASR models for Norwegian.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 28 references that are in the map. First posted on arXiv 4 Jul 2023.
Builds on
9 of 28 references.
References · 9
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
- 2021XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scalecited by 589
- 2022FLEURS: Few-shot Learning Evaluation of Universal Representations of Speechcited by 458
- 2015Deep Speech 2: End-to-End Speech Recognition in English and Mandarincited by 449
- 2021Operationalizing a National Digital Library: The Case for a Norwegian Transformer Modelcited by 23
- 2022The Norwegian Parliamentary Speech Corpuscited by 7
- 2021BERT Attends the Conversation: Improving Low-Resource Conversational ASRcited by 4
- 2022Hearing voices at the National Library -- a speech corpus and acoustic model for the Swedish languagecited by 2
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2 papers.