| Autor: |
Yakovlev, Ivan, Melnikov, Mikhail, Bukhal, Nikita, Makarov, Rostislav, Alenin, Alexander, Torgashov, Nikita, Okhotnikov, Anton |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 6612-6616 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/ICASSP43922.2022.9746527 |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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