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.

该研究提出了一种完全可解释的说话者验证方法，通过从语音记录中自动提取的个人属性（如性别、国籍和年龄）进行比较，以识别说话者。该方法更贴近人类推理，比传统方法更易理解。在 Voxceleb1 测试集上评估，系统的最佳性能与使用所有正确属性时建立的真实性相当，证明了其有效性。尽管与不可解释的方法相比，我们的方法在一些性能上做出了一定的牺牲，但我们相信它使我们更接近透明、可解释的人工智能的目标，并为未来的属性扩展奠定了基础。