New XAI method interprets hierarchical structure of speaker embeddings

Researchers have developed a new method, Hierarchical Cluster-Class Matching (HCCM), to interpret the organization of speaker embeddings within neural networks from an Explainable AI (XAI) perspective. By applying a hierarchical clustering algorithm called Single-Linkage Clustering (SLINK), the study analyzes how speaker embeddings form hierarchical clusters. The HCCM method then evaluates these clusters against semantic classes related to speaker identity, gender, and nationality, using a new metric called the L-score to diagnose imperfect matches and provide insights into the internal semantics of speaker recognition models. AI

IMPACT Provides a novel XAI approach to understand the internal semantics of speaker recognition models.

RANK_REASON Research paper detailing a new method for interpreting AI model internals. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Explainable AI
• Hierarchical Cluster-Class Matching
• Hugging Face
• L-score
• Single-Linkage Clustering
• speaker embeddings

• paper
• other

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