ACM FAccT 2022

Bias in Automated Speaker Recognition


Automated speaker recognition uses data processing 
to identify speakers by their voice. Today, automated 
speaker recognition technologies are deployed on 
billions of smart devices and in services such as call 
centres. Despite their wide-scale deployment and known 
sources of bias in face recognition and natural 
language processing, bias in automated speaker 
recognition has not been studied systematically. We 
present an in-depth empirical and analytical study of 
bias in the machine learning development workflow of 
speaker verification, a voice biometric and core task 
in automated speaker recognition. Drawing on an 
established framework for understanding sources of harm 
in machine learning, we show that bias exists at every 
development stage in the well-known VoxCeleb Speaker 
Recognition Challenge, including model building, 
implementation, and data generation. Most affected are 
female speakers and non-US nationalities, who experience 
significant performance degradation. Leveraging the 
insights from our findings, we make practical 
recommendations for mitigating bias in automated speaker 
recognition, and outline future research directions.

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ACM Library

 author = {Hutiri, Wiebke Toussaint and Ding, Aaron Yi},
 title = {Bias in Automated Speaker Recognition},
 booktitle = {2022 ACM Conference on Fairness, Accountability, and Transparency},
 pages = {230-247},
 numpages = {18},
 series = {FAccT '22},
 year = {2022},
 publisher = {ACM}
How to cite:

Wiebke Toussaint Hutir, Aaron Yi Ding. 2022. "Bias in Automated Speaker Recognition". In Proceedings of the 5th ACM Conference on Fairness, Accountability, and Transparency (FAccT '22).