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Optimization of the DET curve in speaker verification

机译:说话人验证中DET曲线的优化

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Speaker verification systems are, in essence, statistical pattern detectors which can trade off false rejections for false acceptances. Any operating point characterized by a specific tradeoff between false rejections and false acceptances may be chosen. Training paradigms in speaker verification systems however either learn the parameters of the classifier employed without actually considering this tradeoff, or optimize the parameters for a particular operating point exemplified by the ratio of positive and negative training instances supplied. In this paper we investigate the optimization of training paradigms to explicitly consider the tradeoff between false rejections and false acceptances, by minimizing the area under the curve of the detection error tradeoff curve. To optimize the parameters, we explicitly minimize a mathematical characterization of the area under the detection error tradeoff curve, through generalized probabilistic descent. Experiments on the NIST 2008 database show that for clean signals the proposed optimization approach is at least as effective as conventional learning. On noisy data, verification performance obtained with the proposed approach is considerably better than that obtained with conventional learning methods.
机译:说话者验证系统实质上是统计模式检测器,可以将错误拒绝与错误接受权衡。可以选择以错误拒绝和错误接受之间的特定折衷为特征的任何工作点。然而,说话者验证系统中的训练范例要么在不实际考虑折衷的情况下学习所采用分类器的参数,要么针对特定工作点优化参数,以所提供的正负训练实例之比为例。在本文中,我们通过最小化检测误差折衷曲线曲线下的面积,研究了训练范式的优化,以明确考虑错误拒绝和错误接受之间的折衷。为了优化参数,我们通过广义概率下降显式最小化检测误差折衷曲线下面积的数学特征。在NIST 2008数据库上进行的实验表明,对于纯净信号,建议的优化方法至少与常规学习一样有效。在嘈杂的数据上,通过提出的方法获得的验证性能明显优于通过传统学习方法获得的验证性能。

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