license: cc-by-4.0
SEA Fake Speech Dataset
The rapid growth of the digital economy in South-East Asia (SEA) has amplified the risks of audio deepfakes—yet current datasets cover SEA languages only sparsely, leaving models poorly equipped to handle this critical region. This omission is critical: detection models trained on high-resource languages collapse when applied to SEA, due to mismatches in synthesis quality, language-specific characteristics, and data scarcity. To close this gap, we present SEA-Spoof, the first large-scale ADD dataset especially for SEA languages. SEA-Spoof spans 300+ hours of paired real and spoof speech across Tamil, Hindi, Thai, Indonesian, Malay, and Viet- namese. Spoof samples are generated from a diverse mix of state- of-the-art open-source and commercial systems, capturing wide variability in style and fidelity. Benchmarking state-of-the-art detec- tion models reveals severe cross-lingual degradation, but fine-tuning on SEA-Spoof dramatically restores performance across languages and synthesis sources. These results highlight the urgent need for SEA-focused research and establish SEA-Spoof as a foundation for developing robust, cross-lingual, and fraud-resilient detection systems.
Dataset Summary
This dataset contains multilingual speech data for deepfake detection, covering 6 Southeast Asian languages (Hindi, Tamil, Thai, Malay, Indonesian, Vietnamese). It includes synthetic speech from open-source and commercial TTS models, as well as real human recordings.
Languages
- Hindi
- Tamil
- Thai
- Malay
- Indonesian
- Vietnamese