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README.md

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 license: apache-2.0
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+---
+This repository introduces:  🌀 *ShiftySpeech*: A Large-Scale Synthetic Speech Dataset with Distribution Shifts
+
+## 🔥 Key Features
+- 3000+ hours of synthetic speech
+- **Diverse Distribution Shifts**: The dataset spans **7 key distribution shifts**, including:  
+  - 📖 **Reading Style**  
+  - 🎙️ **Podcast**  
+  - 🎥 **YouTube**  
+  - 🗣️ **Languages (Three different languages)**  
+  - 🌎 **Demographics (including variations in age, accent, and gender)**  
+- **Multiple Speech Generation Systems**: Includes data synthesized from various **TTS models** and **vocoders**.
+  
+## 💡 Why We Built This Dataset
+> Driven by advances in self-supervised learning for speech, state-of-the-art synthetic speech detectors have achieved low error rates on popular benchmarks such as ASVspoof. However, prior benchmarks do not address the wide range of real-world variability in speech. Are reported error rates realistic in real-world conditions? To assess detector failure modes and robustness under controlled distribution shifts, we introduce **ShiftySpeech**, a benchmark with more than 3000 hours of synthetic speech from 7 domains, 6 TTS systems, 12 vocoders, and 3 languages.
+>
+> ### **Citation**
+
+If you find this dataset useful, please cite our work:
+```bibtex
+@misc{garg2025syntheticspeechdetectionwild,
+      title={Less is More for Synthetic Speech Detection in the Wild}, 
+      author={Ashi Garg and Zexin Cai and Henry Li Xinyuan and Leibny Paola García-Perera and Kevin Duh and Sanjeev Khudanpur and Matthew Wiesner and Nicholas Andrews},
+      year={2025},
+      eprint={2502.05674},
+      archivePrefix={arXiv},
+      primaryClass={eess.AS},
+      url={https://arxiv.org/abs/2502.05674}, 
+}
+```