Exploring Demographic Effects on Speaker Verification
Sophie Si, Zhengxiong Li, Wenyao Xu · 2021
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.