ShiftySpeech: A Large-Scale Synthetic Speech Dataset with Distribution Shifts

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.