Range-Based Equal Error Rate for Spoof Localization
Lin Zhang Xin Wang Erica Cooper Nicholas Evans Junichi Yamagishi
Spoof localization, also called segment-level detection, is a crucial task
that aims to locate spoofs in partially spoofed audio. The equal error rate
(EER) is widely used to measure performance for such biometric scenarios.
Although EER is the only threshold-free metric, it is usually calculated in a
point-based way that uses scores and references with a pre-defined temporal
resolution and counts the number of misclassified segments. Such point-based
measurement overly relies on this resolution and may not accurately measure
misclassified ranges. To properly measure misclassified ranges and better
evaluate spoof localization performance, we upgrade point-based EER to
range-based EER. Then, we adapt the binary search algorithm for calculating
range-based EER and compare it with the classical point-based EER. Our analyses
suggest utilizing either range-based EER, or point-based EER with a proper
temporal resolution can fairly and properly evaluate the performance of spoof
localization.