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%.
We also experimented with the Google speech recognition api package and other commer- cial tools; however, they generated text with less accuracy and without proper punctuation
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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%.