Explainable AI in Speaker Recognition -- Making Latent Representations Understandable

Neural networks used for speaker recognition organize information in hierarchical patterns: The paper demonstrates that speaker recognition networks don't simply group speakers randomly, but instead create structured, hierarchical relationships in how they represent and distinguish between different speakers—revealing that these "black box" systems have underlying organizational logic that can be uncovered.

New visualization methods can reveal what networks "know" about speaker characteristics: By applying hierarchical clustering algorithms (SLINK and HDBSCAN) rather than simpler clustering approaches, researchers were able to identify how speaker recognition networks naturally organize information related to demographic characteristics like gender and nationality, making previously hidden network knowledge visible and interpretable.

Hierarchical Cluster-Class Matching bridges the gap between mathematical patterns and human understanding: The paper introduces HCCM, an algorithm designed to connect the mathematical clusters discovered in neural networks to meaningful human categories (such as "male," "female," or specific nationalities), helping translate what networks learn into concepts we can understand and evaluate.

This approach enables better auditing of AI systems for bias and fairness: By making visible how speaker recognition networks organize information around demographic characteristics, this work provides a practical tool for identifying whether and how these systems might encode or rely on gender, nationality, or other demographic information—critical for assessing fairness in AI deployment.