Update README.md

README.md

@@ -12,13 +12,28 @@ license: apache-2.0
 
 ## Citation
 ```
-@misc{seo2024manwavmanchuasrmodel,
-      title={ManWav: The First Manchu ASR Model}, 
-      author={Jean Seo and Minha Kang and Sungjoo Byun and Sangah Lee},
-      year={2024},
-      eprint={2406.13502},
-      archivePrefix={arXiv},
-      primaryClass={cs.CL},
-      url={https://arxiv.org/abs/2406.13502}, 
+@inproceedings{seo-etal-2024-manwav,
+    title = "{M}an{W}av: The First {M}anchu {ASR} Model",
+    author = "Seo, Jean  and
+      Kang, Minha  and
+      Byun, SungJoo  and
+      Lee, Sangah",
+    editor = "Serikov, Oleg  and
+      Voloshina, Ekaterina  and
+      Postnikova, Anna  and
+      Muradoglu, Saliha  and
+      Le Ferrand, Eric  and
+      Klyachko, Elena  and
+      Vylomova, Ekaterina  and
+      Shavrina, Tatiana  and
+      Tyers, Francis",
+    booktitle = "Proceedings of the 3rd Workshop on NLP Applications to Field Linguistics (Field Matters 2024)",
+    month = aug,
+    year = "2024",
+    address = "Bangkok, Thailand",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.fieldmatters-1.2",
+    pages = "6--11",
+    abstract = "This study addresses the widening gap in Automatic Speech Recognition (ASR) research between high resource and extremely low resource languages, with a particular focus on Manchu, a severely endangered language. Manchu exemplifies the challenges faced by marginalized linguistic communities in accessing state-of-the-art technologies. In a pioneering effort, we introduce the first-ever Manchu ASR model ManWav, leveraging Wav2Vec2-XLSR-53. The results of the first Manchu ASR is promising, especially when trained with our augmented data. Wav2Vec2-XLSR-53 fine-tuned with augmented data demonstrates a 0.02 drop in CER and 0.13 drop in WER compared to the same base model fine-tuned with original data.",
 }
 ```