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