基于上下文信息与注意力特征的欺骗语音检测

随着语音合成和语音转换技术的快速发展,欺骗语音检测方法仍存在欺骗检测准确率低、通用性差等问题。因此,提出一种基于上下文信息与注意力特征的端到端的欺骗检测方法。该方法基于深度残差收缩网络(DRSN),利用双分支上下文信息协调融合模块(DCCM)聚集丰富的上下文信息,融合基于协调时频注意力机制(CTFA)的特征以获得具有上下文信息的跨维度交互特征,从而最大化捕获伪影的潜力。与最佳基线系统相比,在ASVspoof 2019 LA数据集中,所提方法在EER和t-DCF性能指标上分别降低68%和65%;在ASVspoof 2021 LA数据集中,所提方法的EER和t-DCF分别为4.81和0.3115,分别降低48%和10%。实验结果表明,所提方法能有效提高欺骗语音检测的准确率和泛化能力。

Spoof speech detection based on context information and attention feature

With the rapid development of speech synthesis and speech conversion technology, methods of spoof speech detection still have problems such as low spoof detection accuracy and poor generality.Therefore, an end-to-end spoof detection method based on context information and attention feature was proposed.Based on deep residual shrinkage network (DRSN), the proposed method used the dual-branch context information coordination fusion module (DCCM) to aggregate rich context information, and fused features based on coordinate time-frequency attention (CTFA) to obtain cross-dimensional interaction features with context information, thus maximizing the potential of capturing artifacts.Compared with the best baseline system, in the ASVspoof 2019 LA dataset, the proposed method had reduced the EER and t-DCF performance indicators by 68% and 65% respectively, in the ASVspoof 2021 LA dataset, the EER and t-DCF of the proposed method were 4.81 and 0.311 5 and dropped by 48% and 10% separately.The experimental results show that this method can effectively improve the accuracy and generalization ability of spoof speech detection.