DER is the primary metric to evaluate diarization performance while facing a
dilemma: the errors in short utterances or segments tend to be overwhelmed by
longer ones. Short segments, e.g., `yes' or `no,' still have semantic
information. Besides, DER overlooks errors in less-talked speakers. Although
JER balances speaker errors, it still suffers from the same dilemma.
Considering all those aspects, duration error, segment error, and
speaker-weighted error constituting a complete diarization evaluation, we
propose a Balanced Error Rate (BER) to evaluate speaker diarization. First, we
propose a segment-level error rate (SER) via connected sub-graphs and adaptive
IoU threshold to get accurate segment matching. Second, to evaluate diarization
in a unified way, we adopt a speaker-specific harmonic mean between duration
and segment, followed by a speaker-weighted average. Third, we analyze our
metric via the modularized system, EEND, and the multi-modal method on real
datasets. SER and BER are publicly available at this https URL.

提出了基于平衡误差率的方法来评估说话人分离，该方法包括段错误率、片段级误差率和说话人加权误差率，其中通过使用连接的子图和自适应 IoU 阈值来获得准确的片段匹配而提出了段级错误率（SER）。

说话人分离的平衡误差率

BER: Balanced Error Rate For Speaker Diarization

We propose a novel algorithm for learning fair representations that can
simultaneously mitigate two notions of disparity among different demographic
subgroups in the classification setting. Two key components underpinning the
design of our algorithm are balanced error rate and conditional alignment of
representations. We show how these two components contribute to ensuring
accuracy parity and equalized false-positive and false-negative rates across
groups without impacting demographic parity. Furthermore, we also demonstrate
both in theory and on two real-world experiments that the proposed algorithm
leads to a better utility-fairness trade-off on balanced datasets compared with
existing algorithms on learning fair representations for classification.

本篇研究旨在提出一种新算法，用于学习公平的表征，以在分类环境下同时缓解不同人口统计特征子组之间的两种不平等概念。通过平衡误差率和条件对齐的表征等两个关键组件，它对确保跨组别的准确平等性和平衡假阳性和假阴性率发挥了作用。此外，我们还通过理论和两个实际实验证明，与现有的学习公平表征的算法相比，在平衡数据集上提出的算法可以在保持效用公平性的前提下得到更好的公平性。

有条件的公平表达学习

Conditional Learning of Fair Representations

Statistical algorithms are usually helping in making decisions in many
aspects of our lives. But, how do we know if these algorithms are biased and
commit unfair discrimination of a particular group of people, typically a
minority? \textit{Fairness} is generally studied in a probabilistic framework
where it is assumed that there exists a protected variable, whose use as an
input of the algorithm may imply discrimination. There are different
definitions of Fairness in the literature. In this paper we focus on two of
them which are called Disparate Impact (DI) and Balanced Error Rate (BER). Both
are based on the outcome of the algorithm across the different groups
determined by the protected variable. The relationship between these two
notions is also studied. The goals of this paper are to detect when a binary
classification rule lacks fairness and to try to fight against the potential
discrimination attributable to it. This can be done by modifying either the
classifiers or the data itself. Our work falls into the second category and
modifies the input data using optimal transport theory.

本文首先介绍了统计算法在决策中的应用，提出了公平性的概念，进而研究了不同的公平性定义。特别地，文中重点研究了不平等影响和平衡误差率这两个概念，并探讨了它们之间的关系。其目的在于检测二分类规则是否缺乏公平性，同时修改输入数据以应对潜在的歧视问题。针对后者，本文利用最优传输理论对数据进行修改。