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%.
It uses ffmpeg to download the best audio available in the W A V format, and resample it 16 kHz
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.AS 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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%.