Update README.md

README.md

@@ -8,11 +8,12 @@ license: "cc-by-nc-sa-4.0"
 ---
 
 # wav2vec2-base-sk-17k
-This is a monolingual Slovak Wav2Vec 2.0 base model pre-trained from 17 thousand of hours of Slovak speech.
+This is a monolingual Slovak Wav2Vec 2.0 base model pre-trained from 17 thousand hours of Slovak speech.
+It was introduced in the paper **Transfer Learning of Transformer-Based Speech Recognition Models from Czech to Slovak** accepted for the TSD2023 conference.
 
 This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for speech recognition, a tokenizer should be created, and the model should be fine-tuned on labeled data. 
 
-The model was initialized from Czech pre-trained model [fav-kky/wav2vec2-base-cs-80k-ClTRUS](https://huggingface.co/fav-kky/wav2vec2-base-cs-80k-ClTRUS). We found this cross-language transfer learning approach better than pre-training from scratch. See our paper for details.
+The model was initialized from the Czech pre-trained model [fav-kky/wav2vec2-base-cs-80k-ClTRUS](https://huggingface.co/fav-kky/wav2vec2-base-cs-80k-ClTRUS). We found this cross-language transfer learning approach better than pre-training from scratch. See our paper for details.
 
 ## Pretraining data 
 Almost 18 thousand hours of unlabeled Slovak speech:
@@ -51,29 +52,35 @@ After fine-tuning, the model scored the following results on public datasets:
 See our paper for details.
 
 ## Paper 
-The preprint of our paper (accepted to TSD 2023) is available at https://arxiv.org/abs/2306.04399.
+The paper is available at https://link.springer.com/chapter/10.1007/978-3-031-40498-6_29.
+
+The pre-print of our paper is available at https://arxiv.org/abs/2306.04399.
 
 ## Citation
 If you find this model useful, please cite our paper:
 ```
 @inproceedings{wav2vec2-base-sk-17k,
-  title = {{Transfer Learning of Transformer-based Speech Recognition Models from Czech to Slovak}},
   author = {
-    Jan Lehe\v{c}ka and 
-    Josef V. Psutka and 
-    Josef Psutka
+    Lehe\v{c}ka, Jan and
+    Psutka, Josef V. and
+    Psutka, Josef
   },
-  booktitle = {{Text, Speech, and Dialogue}},
-  publisher = {{Springer International Publishing}},
+  title = {{Transfer Learning of Transformer-Based Speech Recognition Models from Czech to Slovak}},
   year = {2023},
-  note = {(in press)},
-  url = {https://arxiv.org/abs/2306.04399},
+  isbn = {978-3-031-40497-9},
+  publisher = {Springer Nature Switzerland},
+  address = {Cham},
+  url = {https://doi.org/10.1007/978-3-031-40498-6_29},
+  doi = {10.1007/978-3-031-40498-6_29},
+  booktitle = {Text, Speech, and Dialogue: 26th International Conference, TSD 2023, Pilsen, Czech Republic, September 4–6, 2023, Proceedings},
+  pages = {328–338},
+  numpages = {11},
 }
 ```
 
 ## Related papers
 - [INTERSPEECH 2022 - Exploring Capabilities of Monolingual Audio Transformers using Large Datasets in Automatic Speech Recognition of Czech](https://www.isca-speech.org/archive/pdfs/interspeech_2022/lehecka22_interspeech.pdf)
-- INTERSPEECH 2023 - Transformer-based Speech Recognition Models for Oral History Archives in English, German, and Czech
+- [INTERSPEECH 2023 - Transformer-based Speech Recognition Models for Oral History Archives in English, German, and Czech](https://www.isca-archive.org/interspeech_2023/lehecka23_interspeech.pdf)
 
 ## Related models
 - [fav-kky/wav2vec2-base-cs-80k-ClTRUS](https://huggingface.co/fav-kky/wav2vec2-base-cs-80k-ClTRUS)