  Receiver operating characteristic (ROC) and detection error tradeoff (DET)
curves are two widely used evaluation metrics for speaker verification. They
are equivalent since the latter can be obtained by transforming the former's
true positive y-axis to false negative y-axis and then re-scaling both axes by
a probit operator. Real-world speaker verification systems, however, usually
work on part of the ROC curve instead of the entire ROC curve given an
application. Therefore, we propose in this paper to use the area under part of
the ROC curve (pAUC) as a more efficient evaluation metric for speaker
verification. A Mahalanobis distance metric learning based back-end is applied
to optimize pAUC, where the Mahalanobis distance metric learning guarantees
that the optimization objective of the back-end is a convex one so that the
global optimum solution is achievable. To improve the performance of the
state-of-the-art speaker verification systems by the proposed back-end, we
further propose two feature preprocessing techniques based on
length-normalization and probabilistic linear discriminant analysis
respectively. We evaluate the proposed systems on the major languages of NIST
SRE16 and the core tasks of SITW. Experimental results show that the proposed
back-end outperforms the state-of-the-art speaker verification back-ends in
terms of seven evaluation metrics.
