  This paper proposes a fully explainable approach to speaker verification
(SV), a task that fundamentally relies on individual speaker characteristics.
The opaque use of speaker attributes in current SV systems raises concerns of
trust. Addressing this, we propose an attribute-based explainable SV system
that identifies speakers by comparing personal attributes such as gender,
nationality, and age extracted automatically from voice recordings. We believe
this approach better aligns with human reasoning, making it more understandable
than traditional methods. Evaluated on the Voxceleb1 test set, the best
performance of our system is comparable with the ground truth established when
using all correct attributes, proving its efficacy. Whilst our approach
sacrifices some performance compared to non-explainable methods, we believe
that it moves us closer to the goal of transparent, interpretable AI and lays
the groundwork for future enhancements through attribute expansion.
