Update README.md

README.md

@@ -115,4 +115,23 @@ Using these settings, the training might take 3-4 days on an average GPU. You ca
 | learning_rate|Can be increased, maybe as high as 1e-4. Speeds up training but might add instability |
 | epochs| Can be decreased significantly. This is a huge dataset and you might get a decent result already after a couple of epochs|
 
+## Citation
+
+```bibtex
+@inproceedings{de-la-rosa-etal-2023-boosting,
+    title = "Boosting {N}orwegian Automatic Speech Recognition",
+    author = "De La Rosa, Javier  and
+      Braaten, Rolv-Arild  and
+      Kummervold, Per  and
+      Wetjen, Freddy",
+    booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
+    month = may,
+    year = "2023",
+    address = "T{\'o}rshavn, Faroe Islands",
+    publisher = "University of Tartu Library",
+    url = "https://aclanthology.org/2023.nodalida-1.55",
+    pages = "555--564",
+    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.",
+}
+```