Update README.md

README.md

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 # 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,