Update README.md

README.md

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 # IndicSynth
 *A Large-Scale Multilingual Synthetic Speech Dataset for Low-Resource Indian Languages*
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 **🏆 Outstanding Paper Award, ACL 2025**
 
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@@ -71,9 +70,9 @@ dataset_info:
 
 **IndicSynth** is a novel multilingual synthetic speech dataset designed to advance multilingual **audio deepfake detection (ADD)** and **anti-spoofing** research. It covers **12 low-resource Indian languages** and provides both **mimicry** and **diversity** subsets.
 
-> 4,000+ hours of synthetic audio  
-> 12 languages: Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Sanskrit, Tamil, Telugu, Urdu  
-> Useful for ADD, speaker verification (SV), and bias studies  
+- 4,000+ hours of synthetic audio  
+- 12 languages: Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Odia, Punjabi, Sanskrit, Tamil, Telugu, Urdu  
+- Useful for ADD, speaker verification (SV), and bias studies  
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 ## 📂 Dataset Structure
 
@@ -108,8 +107,8 @@ Synthetic data was generated using:
 | vits     | TTS       | Yes        | No          |
 | freevc24 | VC        | No         | No          |
 
-> **Mimicry subset**: For anti-spoofing research
-> **Diversity subset**: Contains diverse set of realistic synthetic voices for multilingual audio deepfake detection research
+- **Mimicry subset**: For anti-spoofing research
+- **Diversity subset**: Contains diverse set of realistic synthetic voices for multilingual audio deepfake detection research
 For more details, please see the Table 1 and Section 3 of our paper: https://aclanthology.org/2025.acl-long.1070.pdf
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@@ -163,7 +162,6 @@ doi = {10.1609/aaai.v37i11.26521}
 For questions or feedback, please feel free to reach out at divyas@iiitd.ac.in.
 
 ## 🙏 Acknowledgments
-> 🌍 ACL Diversity & Inclusion Subsidy for enabling in-person presentation at ACL 2025
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-> 🤝 HuggingFace for dataset hosting support
+- 🌍 ACL Diversity & Inclusion Subsidy for enabling in-person presentation at ACL 2025
+- 🤝 HuggingFace for dataset hosting support