ash56/ShiftySpeech

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.

⚙️ Usage

Ensure that you have soundfile or librosa installed for proper audio decoding:

pip install soundfile librosa

📌 Example: Loading the AISHELL Dataset Vocoded with APNet2

from datasets import load_dataset

dataset = load_dataset("ash56/ShiftySpeech", data_files={"data": f"Vocoders/apnet2/apnet2_aishell_flac.tar.gz"})["data"]

⚠️ Note: It is recommended to load data from a specific folder to avoid unnecessary memory usage.

The source datasets covered by different TTS and Vocoder systems are listed in tts.yaml and vocoders.yaml

📄 More Information

For detailed information on dataset sources and analysis, see our paper: Less is More for Synthetic Speech Detection in the Wild

You can also find the full implementation on GitHub

Citation

If you find this dataset useful, please cite our work:

@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}, 
}

✉️ Contact

If you have any questions or comments about the resource, please feel free to reach out to us at: [email protected] or [email protected]