Update README.md

README.md

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 In this spoof detection dataset, the bonafide speech is resynthesized using various popular neural audio codecs, which are used for compression and low-bandwidth transmission of speech signals. The spoofed speech samples we provide are generated with a selection of popular and well performing language model based speech synthesis methods, which utilize the same codecs as the bonafide audios to obtain discretized speech tokens. This takes the artifacts of the codecs out of the equation and lets a deepfake detection model trained on this data focus purely on higher-level patterns to differentiate the genuine human samples from the faked speech samples.
 
-This dataset can be seen as a very challenging extension to the ASVSpoof 5 dataset [1, 2] that aligns the field more closely with upcoming challenges in the real world. For compatibility, we follow the design of the data and the dev and test splits with 50% of speakers overlapping the train set and 50% being completely unseen. A publication associated with this dataset is currently under review.
+This dataset can be seen as a very challenging extension to the ASVspoof 5 dataset [1, 2] that aligns the field more closely with upcoming challenges in the real world. For compatibility, we follow the design of the data and the dev and test splits with 50% of speakers overlapping the train set and 50% being completely unseen. A publication associated with this dataset is currently under review.
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+To reproduce our results or compare on the exact same splits, please use the version we provide at https://zenodo.org/records/17225924 which is a subset of this repository that is formatted in accordance to the ASVspoof protocols.
 
 | Attacker          | Codec                   | Parameters | Applied to Bonafide | Link                                               |
 |-------------------|-------------------------|------------|---------------------|----------------------------------------------------|