RIRplay: Generation of a Replay Stereo Corpus for Voice Biometrics Anti-Spoofing
José C. Sánchez-Valera, Antonio M. Peinado, Juan M. Martín-Doñas, Alejandro Gomez-Alanis, Angel Manuel Gomez, Massimiliano Todisco · IEEE Transactions on Information Forensics and Security · 2026
While recent efforts in countering spoofing attacks on voice biometric systems have primarily focused on detecting synthetic speech, Physical Access (PA) attacks, such as audio replay, still pose a serious and unresolved challenge. This research gap has been mainly due to the lack of new, realistic speech corpora for training and testing effective and generalizable countermeasure systems. Given the difficulty in collecting actual audio samples from this kind of attack, simulation has been proposed as an alternative to provide audio replay training data. The objective of this work is the generation of a novel simulated database, called RIRplay, that is both realistic, in the sense of reproducing the actual spoofing process, and representative of a wide variety of possible acoustic contexts. Our results show that training with the RIRplay corpus reduces the Equal Error Rate (EER) by nearly 10 percentage points on the challenging ASVspoof 2021 evaluation set, from 36.89% to 28.04%, compared to models trained on the ASVspoof 2019 corpus, demonstrating significant improvements in out-of-domain generalization.