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Synthetic Voice Spoofing Detection Based On Online Hard Example Mining

2022-09-23 13:32:15
Ruohua Zhou, Chenlei Hu, Qiuchen Yu, Yuxuan Du
arXiv_SD
     
Abstract

The automatic speaker verification spoofing (ASVspoof) challenge series is crucial for enhancing the spoofing consideration and the countermeasures growth. Although the recent ASVspoof 2019 validation results indicate the significant capability to identify most attacks, the model's recognition effect is still poor for some attacks. This paper presents the Online Hard Example Mining (OHEM) algorithm for detecting unknown voice spoofing attacks. The OHEM is utilized to overcome the imbalance between simple and hard samples in the dataset. The presented system provides an equal error rate (EER) of 0.77% on the ASVspoof 2019 Challenge logical access scenario's evaluation set.

Abstract (translated)

URL

https://arxiv.org/abs/2209.11585

PDF

https://arxiv.org/pdf/2209.11585.pdf

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