Improve model card: Add Acknowledgement and Citation sections

README.md

@@ -67,4 +67,26 @@ Following is the average word error rate (WER) evaluated on the [ESB datasets](h
 | [lite-whisper-large-v3-turbo](https://huggingface.co/efficient-speech/lite-whisper-large-v3-turbo) | 12.6 | 374M | 172M |
 | [lite-whisper-large-v3-turbo-fast](https://huggingface.co/efficient-speech/lite-whisper-large-v3-turbo-fast) | 20.1 | 313M | 172M |
 |   |   |   |   |
-| [whisper-medium](https://huggingface.co/openai/whisper-medium) | 14.8 | 306M | 457M |
+| [whisper-medium](https://huggingface.co/openai/whisper-medium) | 14.8 | 306M | 457M |
+
+## Acknowledgement
+
+- [OpenAI Whisper](https://github.com/openai/whisper)
+- [MLX Whisper](https://github.com/ml-explore/mlx-examples/tree/main/whisper)
+- [ASR Leaderboard](https://github.com/huggingface/open_asr_leaderboard)
+
+## Citation
+
+If you use LiteASR in your research, please cite the following paper:
+
+```
+@misc{kamahori2025liteasrefficientautomaticspeech,
+      title={LiteASR: Efficient Automatic Speech Recognition and Low-Rank Approximation}, 
+      author={Keisuke Kamahori and Jungo Kasai and Noriyuki Kojima and Baris Kasikci},
+      year={2025},
+      eprint={2502.20583},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2502.20583}, 
+}
+```