  The latest research in the field of voice anti-spoofing (VAS) shows that deep
neural networks (DNN) outperform classic approaches like GMM in the task of
presentation attack detection. However, DNNs require a lot of data to converge,
and still lack generalization ability. In order to foster the progress of
neural network systems, we introduce a Large Replay Parallel Dataset (LRPD)
aimed for a detection of replay attacks. LRPD contains more than 1M utterances
collected by 19 recording devices in 17 various environments. We also provide
an example training pipeline in PyTorch [1] and a baseline system, that
achieves 0.28% Equal Error Rate (EER) on evaluation subset of LRPD and 11.91%
EER on publicly available ASVpoof 2017 [2] eval set. These results show that
model trained with LRPD dataset has a consistent performance on the fully
unknown conditions. Our dataset is free for research purposes and hosted on
GDrive. Baseline code and pre-trained models are available at GitHub.
