Update README.md

README.md

@@ -52,3 +52,30 @@ For training the model, the dataset we selected comprises 17.64k hours of news c
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64df9253cccd823564c3303b/O2RA9TAedIv1OTqgdIap5.png)
 
 ## Citation
+```
+@inproceedings{nandi-etal-2023-pseudo,
+    title = "Pseudo-Labeling for Domain-Agnostic {B}angla Automatic Speech Recognition",
+    author = "Nandi, Rabindra Nath  and
+      Menon, Mehadi  and
+      Muntasir, Tareq  and
+      Sarker, Sagor  and
+      Muhtaseem, Quazi Sarwar  and
+      Islam, Md. Tariqul  and
+      Chowdhury, Shammur  and
+      Alam, Firoj",
+    editor = "Alam, Firoj  and
+      Kar, Sudipta  and
+      Chowdhury, Shammur Absar  and
+      Sadeque, Farig  and
+      Amin, Ruhul",
+    booktitle = "Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)",
+    month = dec,
+    year = "2023",
+    address = "Singapore",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2023.banglalp-1.16",
+    doi = "10.18653/v1/2023.banglalp-1.16",
+    pages = "152--162",
+    abstract = "One of the major challenges for developing automatic speech recognition (ASR) for low-resource languages is the limited access to labeled data with domain-specific variations. In this study, we propose a pseudo-labeling approach to develop a large-scale domain-agnostic ASR dataset. With the proposed methodology, we developed a 20k+ hours labeled Bangla speech dataset covering diverse topics, speaking styles, dialects, noisy environments, and conversational scenarios. We then exploited the developed corpus to design a conformer-based ASR system. We benchmarked the trained ASR with publicly available datasets and compared it with other available models. To investigate the efficacy, we designed and developed a human-annotated domain-agnostic test set composed of news, telephony, and conversational data among others. Our results demonstrate the efficacy of the model trained on psuedo-label data for the designed test-set along with publicly-available Bangla datasets. The experimental resources will be publicly available.https://github.com/hishab-nlp/Pseudo-Labeling-for-Domain-Agnostic-Bangla-ASR",
+}
+```