RAT: Reference-Augmented Training for ASV Anti-Spoofing

The paper introduces Reference-Augmented Training (RAT), a novel spoofing countermeasure architecture that leverages speaker-reference recordings to enhance anti-spoofing performance. RAT achieves state-of-the-art results on the ASVspoof 5 benchmark, with an Equal Error Rate (EER) of 2.57% and a minimum Detection Cost Function (minDCF) of 0.074, outperforming traditional single-utterance baselines and large ensemble systems. This method is significant for practitioners as it demonstrates that training with reference channels can lead to improved deepfake detection capabilities, even when references are not available during inference.