# The Norwegian Parliamentary Speech Corpus

arXiv: https://arxiv.org/abs/2201.10881 · HTML: https://mlsanity.com/paper/2201.10881/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 26 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [BERT Attends the Conversation: Improving Low-Resource Conversational ASR](https://mlsanity.com/paper/2110.02267/) (2021)

## Built on by (most cited first, 7 of 7)

- [A Survey of Corpora for Germanic Low-Resource Languages and Dialects](https://mlsanity.com/paper/2304.09805/) (2023)
- [The ParlaSpeech Collection of Automatically Generated Speech and Text Datasets from Parliamentary Proceedings](https://mlsanity.com/paper/2409.15397/) (2024)
- [The Knesset Corpus: An Annotated Corpus of Hebrew Parliamentary Proceedings](https://mlsanity.com/paper/2405.18115/) (2024)
- [EuroSpeech: A Multilingual Speech Corpus](https://mlsanity.com/paper/2510.00514/) (2025)
- [Whale: Large-Scale multilingual ASR model with w2v-BERT and E-Branchformer with large speech data](https://mlsanity.com/paper/2506.01439/) (2025)
- [Boosting Norwegian Automatic Speech Recognition](https://mlsanity.com/paper/2307.01672/) (2023)
- [Finnish Parliament ASR corpus - Analysis, benchmarks and statistics](https://mlsanity.com/paper/2203.14876/) (2022)

## Abstract (arXiv)

The Norwegian Parliamentary Speech Corpus (NPSC) is a speech dataset with recordings of meetings from Stortinget, the Norwegian parliament. It is the first, publicly available dataset containing unscripted, Norwegian speech designed for training of automatic speech recognition (ASR) systems. The recordings are manually transcribed and annotated with language codes and speakers, and there are detailed metadata about the speakers. The transcriptions exist in both normalized and non-normalized form, and non-standardized words are explicitly marked and annotated with standardized equivalents. To test the usefulness of this dataset, we have compared an ASR system trained on the NPSC with a baseline system trained on only manuscript-read speech. These systems were tested on an independent dataset containing spontaneous, dialectal speech. The NPSC-trained system performed significantly better, with a 22.9% relative improvement in word error rate (WER). Moreover, training on the NPSC is shown to have a "democratizing" effect in terms of dialects, as improvements are generally larger for dialects with higher WER from the baseline system.