一种用于重放语音信号检测的具有判别特征的多分支ResNet

Xingliang Cheng, Mingxing Xu, T. Zheng
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引用次数: 3

摘要

目前,ASV系统的安全性越来越受到关注。重放攻击作为常见的欺骗方法之一,易于实现,但难以检测。许多研究人员专注于设计各种特征来检测重放攻击尝试的失真。基于常数Q变换(CQT)幅度的常数Q倒谱系数(CQCC)是重放检测领域的显著特征之一。然而,它忽略了相位信息,这些信息在回放过程中也可能失真。在这项工作中,我们提出了一种基于CQT的修改组延迟特征(CQTMGD),它可以捕获CQT的相位信息。此外,还提出了一种多分支残差卷积网络ResNeWt,用于区分重放攻击和真实尝试。我们在ASVspoof 2019物理访问数据集中评估了我们的提案。结果表明,CQTMGD的性能优于传统的MGD特征,与其他基于幅度和相位的特征的融合得到了进一步的改进。我们最好的融合系统在评估集上实现了0.0096分钟的tDCF和0.39%的EER,在ASVspoof 2019物理访问挑战中,它的表现优于所有其他最先进的方法。
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A multi-branch ResNet with discriminative features for detection of replay speech signals
Nowadays, the security of ASV systems is increasingly gaining attention. As one of the common spoofing methods, replay attacks are easy to implement but difficult to detect. Many researchers focus on designing various features to detect the distortion of replay attack attempts. Constant-Q cepstral coefficients (CQCC), based on the magnitude of the constant-Q transform (CQT), is one of the striking features in the field of replay detection. However, it ignores phase information, which may also be distorted in the replay processes. In this work, we propose a CQT-based modified group delay feature (CQTMGD) which can capture the phase information of CQT. Furthermore, a multi-branch residual convolution network, ResNeWt, is proposed to distinguish replay attacks from bonafide attempts. We evaluated our proposal in the ASVspoof 2019 physical access dataset. Results show that CQTMGD outperformed the traditional MGD feature, and the fusion with other magnitude-based and phase-based features achieved a further improvement. Our best fusion system achieved 0.0096 min-tDCF and 0.39% EER on the evaluation set and it outperformed all the other state-of-the-art methods in the ASVspoof 2019 physical access challenge.
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来源期刊
APSIPA Transactions on Signal and Information Processing
APSIPA Transactions on Signal and Information Processing ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
8.60
自引率
6.20%
发文量
30
审稿时长
40 weeks
期刊最新文献
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