Probabilistic linear discriminant analysis (PLDA) has been widely used in open-set verification tasks, such as speaker verification. A potential issue of this model is that the training set often contains limited number of classes, which makes the estimation for the between-class variance unreliable. This unreliable estimation often leads to degraded generalization. In this paper, we present an MAP estimation for the between-class variance, by employing an Inverse-Wishart prior. A key problem is that with hierarchical models such as PLDA, the prior is placed on the variance of class means while the likelihood is based on class members, which makes the posterior inference intractable. We derive a simple MAP estimation for such a model, and test it in both PLDA scoring and length normalization. In both cases, the MAP-based estimation delivers interesting performance improvement.
翻译:在公开的核查任务中,如演讲人核查,广泛使用了概率线性线性差异分析(PLDA),这一模式的一个潜在问题是,培训组往往包含数量有限的班级,这使得对阶级间差异的估计不可靠,这种不可靠的估计往往导致普遍化的退化。在本文中,我们通过使用相反的Wishart 之前的计算方法,对阶级间差异进行了MAP的估计。一个关键问题是,如PLDA等等级模型,先以阶级手段的差异为基础,而前者则以阶级成员为基础,而前者则以阶级成员为基础,使得后推推推推法难以处理。我们为这种模型得出简单的MAP估计,并在PLDA评分和长度正常化中测试。在这两种情况下,基于MAP的估计都带来有趣的性能改进。