LRPD: Large Replay Parallel Dataset
Ivan Yakovlev, Mikhail Melnikov, Nikita Bukhal, Rostislav Makarov, Alexander Alenin, Nikita Torgashov, Anton Okhotnikov
Abstract
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 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Baseline code and pre-trained models are available at GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
BibTeX
@inproceedings{icassp2022_lrpdlargereplayp,
title = {LRPD: Large Replay Parallel Dataset},
author = {Ivan Yakovlev and Mikhail Melnikov and Nikita Bukhal and Rostislav Makarov and Alexander Alenin and Nikita Torgashov and Anton Okhotnikov},
booktitle = {ICASSP 2022},
year = {2022}
}