{"_id":"6a220fee0ce6f3816ab0f06d","id":"AffectDF/AffectDF_EmotionSDD","author":"AffectDF","sha":"947e890c7328dc1cf5e9e5ea47fd80acb80423e3","lastModified":"2026-06-05T19:54:22.000Z","private":false,"gated":false,"disabled":false,"tags":["task_categories:audio-classification","language:en","license:cc-by-nc-4.0","size_categories:100K<n<1M","format:webdataset","modality:audio","modality:text","library:datasets","library:webdataset","library:mlcroissant","region:us","speech-deepfake-detection","audio-deepfake-detection","emotional-speech","spoofing-detection","speech-synthesis"],"description":"\n\t\n\t\t\n\t\n\t\n\t\tAffectDF: Emotionally Expressive Speech Deepfake Benchmark\n\t\n\n\n\t\n\t\t\n\t\n\t\n\t\tOverview\n\t\n\nAffectDF is a large-scale benchmark for speech deepfake detection under emotionally expressive spoofing conditions. The dataset is designed to evaluate whether current speech deepfake detection (SDD) systems can generalize beyond conventional neutral-speech benchmarks to modern emotional and expressive speech attacks.\nAffectDF contains approximately 260 hours of audio generated using 21 spoofing… See the full description on the dataset page: https://huggingface.co/datasets/AffectDF/AffectDF_EmotionSDD.","downloads":94,"likes":0,"cardData":{"license":"cc-by-nc-4.0","language":["en"],"task_categories":["audio-classification"],"tags":["speech-deepfake-detection","audio-deepfake-detection","emotional-speech","spoofing-detection","speech-synthesis"],"pretty_name":"AffectDF EmotionSDD"},"siblings":[{"rfilename":".gitattributes"},{"rfilename":"Dev.tar.gz"},{"rfilename":"Protocols.tar.gz"},{"rfilename":"README.md"},{"rfilename":"Test.tar.gz"},{"rfilename":"Train.tar.gz"}],"createdAt":"2026-06-04T23:53:18.000Z","usedStorage":45228942270}