  When decisions are made and when personal data is treated by automated
processes, there is an expectation of fairness -- that members of different
demographic groups receive equitable treatment. This expectation applies to
biometric systems such as automatic speaker verification (ASV). We present a
comparison of three candidate fairness metrics and extend previous work
performed for face recognition, by examining differential performance across a
range of different ASV operating points. Results show that the Gini Aggregation
Rate for Biometric Equitability (GARBE) is the only one which meets three
functional fairness measure criteria. Furthermore, a comprehensive evaluation
of the fairness and verification performance of five state-of-the-art ASV
systems is also presented. Our findings reveal a nuanced trade-off between
fairness and verification accuracy underscoring the complex interplay between
system design, demographic inclusiveness, and verification reliability.
