Update README.md

README.md

@@ -38,7 +38,7 @@ When using the model make sure that your speech input is also sampled at 16Khz.
 The Sámi Wav2Vec2 Large has the same architecture and uses the same training objective as the English and multilingual one described in [Paper](https://arxiv.org/abs/2006.11477).
 
 [GetmanY1/wav2vec2-large-sami-22k](https://huggingface.co/GetmanY1/wav2vec2-large-sami-22k) is a large-scale, 317-million parameter monolingual model pre-trained on 22.4k hours of unlabeled Sámi speech from [KAVI radio and television archive materials](https://kavi.fi/en/radio-ja-televisioarkistointia-vuodesta-2008/).
-You can read more about the pre-trained model from [this paper](TODO).
+You can read more about the pre-trained model from [this paper](https://aclanthology.org/2025.nodalida-1.19/).
 
 The model was evaluated on 1 hour of out-of-domain read-aloud and spontaneous speech of varying audio quality.
 
@@ -72,7 +72,7 @@ transcription = processor.batch_decode(predicted_ids)
 
 ### Prefix Beam Search
 
-In our experiments (see [paper](TODO)), we observed a slight improvement in terms of Character Error Rate (CER) when using prefix beam search compared to greedy decoding, primarily due to a reduction in deletions. Below is our adapted version of [corticph/prefix-beam-search](https://github.com/corticph/prefix-beam-search) for use with wav2vec 2.0 in HuggingFace Transformers.
+In our experiments (see [paper](https://aclanthology.org/2025.nodalida-1.19/)), we observed a slight improvement in terms of Character Error Rate (CER) when using prefix beam search compared to greedy decoding, primarily due to a reduction in deletions. Below is our adapted version of [corticph/prefix-beam-search](https://github.com/corticph/prefix-beam-search) for use with wav2vec 2.0 in HuggingFace Transformers.
 Note that an external language model (LM) **is not required**, as the function defaults to a uniform probability when none is provided.
 
 ```
@@ -175,6 +175,31 @@ def map_to_pred_prefix_beam_search(batch):
 result = ds.map(map_to_pred_prefix_beam_search, batched=True, batch_size=1, remove_columns=["speech"])
 ```
 
+## Citation
+
+If you use our models or scripts, please cite our article as:
+
+```bibtex
+@inproceedings{getman-etal-2025-towards,
+    title = "Towards large-scale speech foundation models for a low-resource minority language",
+    author = "Getman, Yaroslav  and
+      Gr{\'o}sz, Tam{\'a}s  and
+      Hiovain-Asikainen, Katri  and
+      Lehtonen, Tommi  and
+      Kurimo, Mikko",
+    editor = "Johansson, Richard  and
+      Stymne, Sara",
+    booktitle = "Proceedings of the Joint 25th Nordic Conference on Computational Linguistics and 11th Baltic Conference on Human Language Technologies (NoDaLiDa/Baltic-HLT 2025)",
+    month = mar,
+    year = "2025",
+    address = "Tallinn, Estonia",
+    publisher = "University of Tartu Library",
+    url = "https://aclanthology.org/2025.nodalida-1.19/",
+    pages = "192--200",
+    ISBN = "978-9908-53-109-0"
+}
+```
+
 ## Team Members
 
 - Yaroslav Getman, [Hugging Face profile](https://huggingface.co/GetmanY1), [LinkedIn profile](https://www.linkedin.com/in/yaroslav-getman/)