# Boosting Norwegian Automatic Speech Recognition

arXiv: https://arxiv.org/abs/2307.01672 · HTML: https://mlsanity.com/paper/2307.01672/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech](https://mlsanity.com/paper/2205.12446/) (2022)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [Operationalizing a National Digital Library: The Case for a Norwegian Transformer Model](https://mlsanity.com/paper/2104.09617/) (2021)
- [The Norwegian Parliamentary Speech Corpus](https://mlsanity.com/paper/2201.10881/) (2022)
- [BERT Attends the Conversation: Improving Low-Resource Conversational ASR](https://mlsanity.com/paper/2110.02267/) (2021)
- [Hearing voices at the National Library -- a speech corpus and acoustic model for the Swedish language](https://mlsanity.com/paper/2205.03026/) (2022)

## Built on by (most cited first, 2 of 2)

- [Whispering in Norwegian: Navigating Orthographic and Dialectic Challenges](https://mlsanity.com/paper/2402.01917/) (2024)
- [Non-native Children's Automatic Speech Assessment Challenge (NOCASA)](https://mlsanity.com/paper/2504.20678/) (2025)

## 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.