A speech-deepfake dataset for ten public figures built with transcription-based segmentation reports high synthetic naturalness (NISQA 3.69) and a human misclassification rate of 61.9%.
This high quality of synthesized speech and the ability to distribute it through so- cial media platforms are giving rise to manipulated information in the digital ecosystem
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Collecting, Curating, and Annotating Good Quality Speech deepfake dataset for Famous Figures: Process and Challenges
A speech-deepfake dataset for ten public figures built with transcription-based segmentation reports high synthetic naturalness (NISQA 3.69) and a human misclassification rate of 61.9%.