The data dependency due to multiple use of the same subjects has impact on the standard error (SE) of the detection cost function (DCF) in speaker recognition evaluation. The DCF is defined as a weighted sum of the probabilities of type I and type II errors at a given threshold. A two-layer data structure is constructed: target scores are grouped into target sets based on the dependency, and likewise for non-target scores. On account of the needed equal probabilities for scores being selected when resampling, target sets must contain the same number of target scores, and so must non-target sets. In addition to the bootstrap method with i.i.d. assumption, the nonparametric two-sample one-layer and two-layer bootstrap methods are carried out based on whether the resampling takes place only on sets, or subsequently on scores within the sets. Due to the stochastic nature of the bootstrap, the distributions of the SEs of the DCF estimated using the three different bootstrap methods are created and compared. After performing hypothesis testing, it is found that data dependency increases not only the SE but also the variation of the SE, and the two-layer bootstrap is more conservative than the one-layer bootstrap. The rationale regarding the different impacts of the three bootstrap methods on the estimated SEs is investigated.

中文翻译：

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数据依赖对说话人识别评估的影响。

由于多次使用相同的主题而导致的数据依赖性会影响说话人识别评估中检测成本函数（DCF）的标准误差（SE）。DCF定义为在给定阈值下I型和II型错误概率的加权和。构造了一个两层的数据结构：目标得分基于相关性分组为目标集，同样对于非目标得分也是如此。考虑到重新采样时选择得分所需的相等概率，目标集必须包含相同数量的目标得分，非目标集也必须包含相同数量的目标得分。除了具有iid假设的自举方法外，还基于重采样是仅对集合进行采样，还是随后对集合中的分数进行非参数的两样本一层和两层自举方法。由于引导程序的随机性，创建并比较了使用三种不同的引导程序方法估计的DCF的SE分布。在进行假设检验之后，发现数据依赖性不仅增加了SE，而且还增加了SE的变化，并且两层引导程序比一层引导程序更保守。研究了关于三种引导方法对估计的SE的不同影响的基本原理。