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arXiv · 2309.17298

LRPD: Large Replay Parallel Dataset

Ivan Yakovlev·Mikhail Melnikov·Nikita Bukhal·Rostislav Makarov·Alexander Alenin·Nikita Torgashov·Anton Okhotnikov
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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. Baseline code and pre-trained models are available at GitHub.

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Ivan Yakovlev, Mikhail Melnikov, Nikita Bukhal, Rostislav Makarov, Alexander Alenin, Nikita Torgashov, Anton Okhotnikov. 2023-09-29. LRPD: Large Replay Parallel Dataset. https://doi.org/10.1109/icassp43922.2022.9746527

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