Update README.md

README.md

@@ -236,3 +236,22 @@ The following hyperparameters were used during training:
 - Pytorch 2.2.0+cu121
 - Datasets 2.20.0
 - Tokenizers 0.19.1
+
+
+## Citation
+
+```
+@inproceedings{moslem-2024-leveraging,
+    title = "Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation",
+    author = "Moslem, Yasmin",
+    booktitle = "Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024)",
+    month = aug,
+    year = "2024",
+    address = "Bangkok, Thailand (in-person and online)",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.iwslt-1.31/",
+    doi = "10.18653/v1/2024.iwslt-1.31",
+    pages = "265--273",
+    abstract = "This paper describes our system submission to the International Conference on Spoken Language Translation (IWSLT 2024) for Irish-to-English speech translation. We built end-to-end systems based on Whisper, and employed a number of data augmentation techniques, such as speech back-translation and noise augmentation. We investigate the effect of using synthetic audio data and discuss several methods for enriching signal diversity."
+}
+```