Exploring Demographic Effects on Speaker Verification
Exploring Demographic Effects on Speaker Verification
复制标题
探索人口统计对说话人验证的影响
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Wenyao Xu
中科院分区：
文献类型：
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作者：
Sophie Si;Zhengxiong Li;Wenyao Xu
语音生物识别（例如，说话人验证）是一种基于人类语音特征的关键类型的生物识别技术，以安全性和用户友好性而闻名。它已广泛应用于世界范围内的应用，如语音助手和网上银行。然而，一个关于人口统计公平性的关注迅速上升，不同的子组可能有不同的说话人确认性能，由于固有的语音特征。很少有工作做调查这一问题。招募了一个由300名不同种族和性别的演讲者组成的多元化小组进行探索。在运行了一些说话人验证评估之后，得出了三个结论。首先，在美国四大种族（白色、黑人、拉丁裔和亚洲人）中，拉丁裔在说话人确认中表现最差。第二，男女之间的表现差别不大。第三，高熵语音在说话人确认性能上优于低熵语音。
Voice biometrics (e.g., Speaker Verification) is a critical type of biometrics based on human voice characteristics and is known for security and user-friendliness. It has been widely applied in worldwide applications, such as voice assistants and online banking. However, a concern is raised rapidly about the demographic fairness that different subgroups may have different speaker verification performance due to the inherent voice characteristics. And little work done investigates this concern. A diverse group of 300 speakers by race and gender is recruited for exploration. After running some speaker verification evaluations, three conclusions were reached. Firstly, the Latinx are performed the worst among the four major races in the US (White, Black, Latinx, and Asian) in speaker verification. Secondly, that gender shows little difference in performance between men and women. Thirdly, that high entropy voices performed better than low entropy voices in speaker verification performance.