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Update README.md

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1. Add OTC paper link
2. Add OTC bibtex
3. Add pre-trained model link
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# 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}
}
```