Exploring Algorithmic Fairness in Deep Speaker Verification
Exploring Algorithmic Fairness in Deep Speaker Verification
复制标题
探索深度说话人验证中的算法公平性
DOI：
--
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
M. Marras
中科院分区：
文献类型：
--
作者：
G. Fenu;Hicham Lafhouli;M. Marras
为了允许个人完成基于语音的任务（例如，发送信息或进行支付），现代自动化系统需要将说话者的声音与唯一的数字身份表示进行匹配，以进行验证。尽管到目前为止实现了越来越多的准确性，但仍然没有充分探索这种系统所做的决定如何受到所考虑的个人的固有特征的影响。在本文中，我们研究了最先进的说话人验证模型是如何对受法律保护的个人类别不公平的，其特征在于一个共同的敏感属性（即，性别、年龄、语言）。为此，我们首先安排了一个语音数据集，目的是包括和识别各种人口统计类别。然后，我们在不同的级别上进行了性能分析，从相等的错误率到验证分数分布。实验表明，属于某些人口群体的个人系统性地经历更高的错误率，这凸显了需要更公平的说话者识别模型，进而需要适当的评估框架。
To allow individuals to complete voice-based tasks (e.g., send messages or make payments), modern automated systems are required to match the speaker’s voice to a unique digital identity representation for verification. Despite the increasing accuracy achieved so far, it still remains under-explored how the decisions made by such systems may be influenced by the inherent characteristics of the individual under consideration. In this paper, we investigate how state-of-the-art speaker verification models are susceptible to unfairness towards legally-protected classes of individuals, characterized by a common sensitive attribute (i.e., gender, age, language). To this end, we first arranged a voice dataset, with the aim of including and identifying various demographic classes. Then, we conducted a performance analysis at different levels, from equal error rates to verification score distributions. Experiments show that individuals belonging to certain demographic groups systematically experience higher error rates, highlighting the need of fairer speaker recognition models and, by extension, of proper evaluation frameworks.
大数据警务的不同影响
DOI：
--
发表时间：
2017
期刊：
Georgia law review
影响因子：
--
作者：
Selbst, Andrew D.
通讯作者：
Selbst, Andrew D.