RIRplay: A Replay Stereo Corpus for Voice Biometrics Anti-Spoofing

This is the Zenodo repository for the RIRplay dataset. RIRplay is a novel simulated corpus for Physical Access (PA) spoofing detection, created to support research on replay attack countermeasures for voice biometric systems. Unlike existing replay corpora, which are often limited in size, diversity, or realism, RIRplay reproduces the full replay attack pipeline through simulation, including realistic room acoustics, varied playback and recording conditions, and non-linear device effects. The database has been developed to address the lack of representative training data for replay detection and to improve the robustness and generalization of anti-spoofing systems against replay attacks in unseen scenarios. Results on benchmarks including ASVspoof 2021 and ReMASC show that models trained with RIRplay clearly outperform those trained on previous corpora, such as ASVspoof 2019, confirming its value as a scalable resource for the development of replay attack detection systems.

More details about the generation process and the conducted experiments can be found in the published IEEE Transactions on Information Forensics and Security paper:

J. C. Sanchez-Valera, A. M. Peinado, J. M. Martin-Doñas, A. Gomez-Alanis, A. M. Gomez, and M. Todisco, "RIRplay: Generation of a replay stereo corpus for voice biometrics anti-spoofing," IEEE Transactions on Information Forensics and Security, 2026.

We would greatly appreciate it if you cite the aforementioned journal article, rather than only the Zenodo repository, when using this dataset or comparing your results with ours:

@ARTICLE{sanchezvalera26,
author={Sanchez-Valera, Jose C. and Peinado, Antonio M. and Martin-Doñas, Juan M. and Gomez-Alanis, Alejandro and Gomez, Angel M. and Todisco, Massimiliano},
journal={IEEE Transactions on Information Forensics and Security},
title={RIRplay: Generation of a Replay Stereo Corpus for Voice Biometrics Anti-Spoofing},
year={2026},
volume={21},
number={},
pages={4106-4118},
doi={10.1109/TIFS.2026.3684815}
}

Version v0.0 release notes (This version)

This initial release of RIRplay corresponds to the generated dataset used to obtain all the results reported in the paper for the Conformer and AASIST architectures. Due to data loss affecting one of the storage devices, this release only provides the files RIRplay-PA-*_090426_ASASVI_V0.0_metadata.txt, which contain the metadata required to reproduce and carry out reproducibility tests of the EER results reported in the paper, including file names and labels. The remaining information related to the acoustic generation process is not available for this initial release.

Important notes:

All audio files are provided in .FLAC format.

The sampling rate is 16 kHz.

Only two classes are considered: bona fide and spoofed audio.

To improve the robustness and generalization capability of the DNNs, silence segments at the beginning and end of the recordings were removed using sileroVAD.

Important version update v0.5

Version v0.5 fixes clipping and short-duration issues detected in some audio files after an exhaustive audio analysis. This new release normalizes spoofed audio signals to unit amplitude before applying the loudspeaker non-linear processing. In addition, it adopts a new strategy regarding VAD trimming: if the default silence-detection threshold (0.5) excessively removes regions that actually contain speech, the audio is kept as originally generated.

This version also includes the files containing all metadata used to acoustically simulate the different generation parameters for bona fide and spoofed samples, as well as the correspondence information required to leverage the stereo feature of the dataset.

For more information and download access, please visit the following link: https://zenodo.org/records/19569123

Acknowledgments and funding

The associated publication is part of the project PID2022-138711OB-I00 funded by MICIU/AEI/10.13039/501100011033 and by ERDF/EU. This dataset follows the Data Management Plan of the ASASVI project (Signal and Neural Processing against Spoofing Attacks and Deepfakes for Secure Voice Interaction, PID2022-138711OB-I00). Its ASASVI dataset identifier is RIRplay-PA-*_090426_ASASVI_V0.0, and the corresponding DMP metadata record is included as RIRplay-PA_090426_ASASVI_V0.0.xml.

Please check LICENSE.txt file before downloading the database.