  Automated speaker recognition uses data processing to identify speakers by
their voice. Today, automated speaker recognition is deployed on billions of
smart devices and in services such as call centres. Despite their wide-scale
deployment and known sources of bias in related domains like 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 data generation,
model building, and implementation. 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.
