论文标题
关于使用强化学习优化串联扬声器验证和对策系统的初步调查
An initial investigation on optimizing tandem speaker verification and countermeasure systems using reinforcement learning
论文作者
论文摘要
自动扬声器验证（ASV）中的欺骗对策（CM）系统通常不用于彼此隔离。可以将这些系统组合到级联的系统中，在该系统中，CM首先决定输入是合成还是真正的语音。如果CM确定它是真正的样本，则ASV系统将考虑使用扬声器验证。系统的最终用户对单个子模型的性能不感兴趣，而是对组合系统的性能感兴趣。可以通过串联检测成本函数（T-DCF）度量来评估这种组合，但是使用自己的性能指标对单个组件进行了分开培训。在这项工作中，我们一起研究培训ASV和CM组件，以使用强化学习来进行更好的T-DCF度量。我们证明，此类培训程序确实能够提高组合系统的性能，并且与我们比较的标准监督学习技术相比，取得了更可靠的结果。
The spoofing countermeasure (CM) systems in automatic speaker verification (ASV) are not typically used in isolation of each other. These systems can be combined, for example, into a cascaded system where CM produces first a decision whether the input is synthetic or bona fide speech. In case the CM decides it is a bona fide sample, then the ASV system will consider it for speaker verification. End users of the system are not interested in the performance of the individual sub-modules, but instead are interested in the performance of the combined system. Such combination can be evaluated with tandem detection cost function (t-DCF) measure, yet the individual components are trained separately from each other using their own performance metrics. In this work we study training the ASV and CM components together for a better t-DCF measure by using reinforcement learning. We demonstrate that such training procedure indeed is able to improve the performance of the combined system, and does so with more reliable results than with the standard supervised learning techniques we compare against.
