arXiv Open Access 2023

Boosting Norwegian Automatic Speech Recognition

Javier de la Rosa Rolv-Arild Braaten Per Egil Kummervold Freddy Wetjen Svein Arne Brygfjeld

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Abstrak

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.

Topik & Kata Kunci

cs.CL

Penulis (5)

Javier de la Rosa

Rolv-Arild Braaten

Per Egil Kummervold

Freddy Wetjen

Svein Arne Brygfjeld

Format Sitasi

APA MLA BibTeX

Rosa, J.d.l., Braaten, R., Kummervold, P.E., Wetjen, F., Brygfjeld, S.A. (2023). Boosting Norwegian Automatic Speech Recognition. https://arxiv.org/abs/2307.01672

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓