论文标题
第一个欺骗感知的扬声器验证挑战的基线系统：得分和嵌入融合
Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion
论文作者
论文摘要
深度学习在研究自动扬声器验证（ASV）和欺骗对策（CM）方面带来了令人印象深刻的进步。尽管解决方案是相互依赖的，但它们通常是作为独立子系系统演变而来的，因此CM解决方案通常是为固定ASV系统设计的。本文报告的工作旨在衡量可靠性的提高，这些可靠性可以从它们的紧密整合中获得。使用流行的ASVSPOOF2019数据集得出的结果表明，当评估协议通过欺骗性试验扩展时，最先进的ASV系统的同等错误率（EER）从1.63％降低至23.83％。％受到欺骗攻击的％。但是，即使以分数和基于Deep神经网络的融合策略的形式，ASV和CM系统的直接集成分别将EER降低到1.71％和6.37％。已经形成了新的欺骗意识的说话者验证（SASV）挑战，以鼓励更多地关注ASV和CM系统的整合，并提供一种基准基准不同解决方案的手段。
Deep learning has brought impressive progress in the study of both automatic speaker verification (ASV) and spoofing countermeasures (CM). Although solutions are mutually dependent, they have typically evolved as standalone sub-systems whereby CM solutions are usually designed for a fixed ASV system. The work reported in this paper aims to gauge the improvements in reliability that can be gained from their closer integration. Results derived using the popular ASVspoof2019 dataset indicate that the equal error rate (EER) of a state-of-the-art ASV system degrades from 1.63% to 23.83% when the evaluation protocol is extended with spoofed trials.%subjected to spoofing attacks. However, even the straightforward integration of ASV and CM systems in the form of score-sum and deep neural network-based fusion strategies reduce the EER to 1.71% and 6.37%, respectively. The new Spoofing-Aware Speaker Verification (SASV) challenge has been formed to encourage greater attention to the integration of ASV and CM systems as well as to provide a means to benchmark different solutions.
