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README.md

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 # ReplayDF
 
-**ReplayDF** is a dataset for evaluating the impact of **replay attacks** on audio deepfake detection systems. It features re-recorded synthetic speech derived from [M-AILABS](https://github.com/imdatceleste/m-ailabs-dataset) and [MLAAD v5](https://deepfake-total.com/mlaad), using **109 unique speaker-microphone combinations** across six languages and four TTS models in diverse acoustic environments.
+**ReplayDF** is a dataset for evaluating the impact of **replay attacks** on audio deepfake detection systems. 
+It features re-recorded bona-fide and synthetic speech derived from [M-AILABS](https://github.com/imdatceleste/m-ailabs-dataset) and [MLAAD v5](https://deepfake-total.com/mlaad), using **109 unique speaker-microphone combinations** across six languages and four TTS models in diverse acoustic environments.
 
-This dataset reveals how such replays can significantly degrade the performance of state-of-the-art detectors. It is provided for **non-commercial research** to support the development of **robust and generalizable** deepfake detection systems.
+This dataset reveals how such replays can significantly degrade the performance of state-of-the-art detectors. 
+That is, audio deepfakes are detected much worse once they have been played over a loudspeaker and re-recorded via a microphone.
+It is provided for **non-commercial research** to support the development of **robust and generalizable** deepfake detection systems.
 
 ## 📄 Paper
 [Replay Attacks Against Audio Deepfake Detection (Interspeech 2025)](https://arxiv.org/pdf/2505.14862)