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.

翻译：伪造定位，也称为片段级检测，是一项关键任务，旨在定位部分伪造音频中的伪造部分。等错误率（EER）被广泛用于衡量此类生物特征场景的性能。尽管EER是唯一无需阈值的指标，但它通常以基于点的方式计算，即使用预定义时间分辨率的分数和参考值，并统计误分类片段的数量。这种基于点的测量过度依赖该分辨率，且可能无法准确衡量误分类的范围。为了正确衡量误分类范围并更好地评估伪造定位性能，我们将基于点的EER升级为基于范围的EER。随后，我们调整了用于计算基于范围EER的二分搜索算法，并将其与经典的基于点EER进行比较。我们的分析表明，使用基于范围的EER或具有适当时间分辨率的基于点EER，可以公平且恰当地评估伪造定位的性能。