论文标题
SA-SASV：端到端的欺骗席卷欺骗的扬声器验证系统
SA-SASV: An End-to-End Spoof-Aggregated Spoofing-Aware Speaker Verification System
论文作者
论文摘要
在过去的几年中，研究促进了自动扬声器验证系统和对策系统的性能，以在每个系统上提供低相等的错误率（EER）。但是，对两个系统的联合优化的研究仍然有限。提出了欺骗意识的说话者验证（SASV）2022挑战，以鼓励开发具有新指标的综合SASV系统来评估联合模型性能。本文提出了一种无合奏的端到端解决方案，称为SpoOf-gregregated-SASV（SA-SASV），以构建具有多任务分类器的SASV系统，该系统通过多个损失进行了优化，并且在训练集中具有更灵活的要求。拟议的系统在ASVSPOOF 2019 LA DataSet上进行了培训，该数据集是一个具有少量真正的扬声器的欺骗验证数据集。 SASV-EER的结果表明，可以通过在完整的自动扬声器验证和对策数据集中培训来进一步提高模型性能。
Research in the past several years has boosted the performance of automatic speaker verification systems and countermeasure systems to deliver low Equal Error Rates (EERs) on each system. However, research on joint optimization of both systems is still limited. The Spoofing-Aware Speaker Verification (SASV) 2022 challenge was proposed to encourage the development of integrated SASV systems with new metrics to evaluate joint model performance. This paper proposes an ensemble-free end-to-end solution, known as Spoof-Aggregated-SASV (SA-SASV) to build a SASV system with multi-task classifiers, which are optimized by multiple losses and has more flexible requirements in training set. The proposed system is trained on the ASVSpoof 2019 LA dataset, a spoof verification dataset with small number of bonafide speakers. Results of SASV-EER indicate that the model performance can be further improved by training in complete automatic speaker verification and countermeasure datasets.
