diff --git a/egs/librispeech/WSASR/README.md b/egs/librispeech/WSASR/README.md
index 0a06c7984..3b8822fd2 100644
--- a/egs/librispeech/WSASR/README.md
+++ b/egs/librispeech/WSASR/README.md
@@ -1,7 +1,7 @@
 # Introduction
 
 This is a weakly supervised ASR recipe for the LibriSpeech (clean 100 hours) dataset. We train a
-conformer model using [Bypass Temporal Classification](https://arxiv.org/pdf/2306.01031.pdf) (BTC)/Omni-temporal Classification (OTC) with transcripts with synthetic errors. In this README, we will describe
+conformer model using [Bypass Temporal Classification](https://arxiv.org/pdf/2306.01031.pdf) (BTC)/[Omni-temporal Classification](https://arxiv.org/pdf/2309.15796.pdf) (OTC) with transcripts with synthetic errors. In this README, we will describe
 the task and the BTC/OTC training process.
 
 Note that OTC is an extension of BTC and supports all BTC functions. Therefore, in the following, we only describe OTC.
@@ -203,6 +203,9 @@ export CUDA_VISIBLE_DEVICES="0"
   </tr>
 </table>
 
+## Pre-trained Model
+Pre-trained model: <https://huggingface.co/dgao/icefall-otc-librispeech-conformer-ctc2>
+
 ## Citations
 ```
 @inproceedings{gao2023bypass,
@@ -211,4 +214,11 @@ export CUDA_VISIBLE_DEVICES="0"
   booktitle={INTERSPEECH},
   year={2023}
 }
+
+@inproceedings{gao2023learning,
+  title={Learning from Flawed Data: Weakly Supervised Automatic Speech Recognition},
+  author={Gao, Dongji and Xu, Hainan and Raj, Desh and Garcia, Leibny Paola and Povey, Daniel and Khudanpur, Sanjeev},
+  booktitle={IEEE ASRU},
+  year={2023}
+}
 ```