论文标题
跨域标签的挤压值：扬声器验证的分离评分方法
Squeezing value of cross-domain labels: a decoupled scoring approach for speaker verification
论文作者
论文摘要
域不匹配通常发生在实际应用中，并导致说话者验证系统的严重降低性能。普遍的智慧是收集跨域数据并训练多域PLDA模型，希望学习独立于域的扬声器子空间。在本文中，我们首先提出了一项实证研究，以表明仅添加跨域数据并不能在注册测试不匹配的情况下有助于表现。仔细的分析表明，这种惊人的结果是由入学率和测试条件之间的不连贯统计引起的。基于此分析，我们提出了一种分离的评分方法，该方法可以最大程度地挤压跨域标签的值并在注册和测试不匹配时获得最佳验证分数。当统计数据连贯时，新的配方又落回了常规PLDA。跨通道测试的实验结果表明，所提出的方法是非常有效的，是域不匹配的原理解决方案。
Domain mismatch often occurs in real applications and causes serious performance reduction on speaker verification systems. The common wisdom is to collect cross-domain data and train a multi-domain PLDA model, with the hope to learn a domain-independent speaker subspace. In this paper, we firstly present an empirical study to show that simply adding cross-domain data does not help performance in conditions with enrollment-test mismatch. Careful analysis shows that this striking result is caused by the incoherent statistics between the enrollment and test conditions. Based on this analysis, we present a decoupled scoring approach that can maximally squeeze the value of cross-domain labels and obtain optimal verification scores when the enrollment and test are mismatched. When the statistics are coherent, the new formulation falls back to the conventional PLDA. Experimental results on cross-channel test show that the proposed approach is highly effective and is a principle solution to domain mismatch.
