Speaker embeddings reveal hierarchical clusters linked to identity and traits
Interpreting hierarchical organisation of speaker embeddings
Artificial Intelligence
Summary
Speaker recognition systems learn hidden patterns from voices to tell who is speaking. This paper looks inside those patterns to see how the system groups voices in layers, like branches on a tree. The authors used a special clustering method to find these layers and then matched the clusters to real-world traits like gender and nationality. This helps explain what the system has learned about speakers beyond just their identity. Their work adds a way to understand speaker embeddings more clearly by linking clusters to meaningful categories.
What this means in practice
- •For voice biometric system builders: Improve voice identity systems by interpreting how embeddings group speakers by traits like gender or nationality for more transparent models.
- •For speech technology developers: Develop better voice-based services by understanding hierarchical relationships in speaker embeddings to inform feature design or model tuning.
Authors
Yanze Xu, Wenwu Wang, Mark D. Plumbley
Abstract
Speaker recognition neural networks learn latent representations (i.e. speaker embeddings) from input utterances to recognise speaker identities. However, the internal mechanisms of these networks remain largely opaque, motivating research in explainable artificial intelligence (XAI) to understand them. Nevertheless, existing studies have analysed how speaker embeddings are organised, but rarely frame these analyses within XAI. Hence, this work proposes to explain and interpret the organisation of speaker embeddings from an XAI perspective. To this end, we apply a hierarchical clustering algorithm, Single-Linkage Clustering (SLINK), to analyse whether some speaker embeddings naturally form clusters with hierarchical relationships. The resulting hierarchical organisation (i.e. hierarchical clusters) is evaluated using the Cluster-Class Matching (CCM) method. Moreover, we propose a new method, termed Hierarchical Cluster-Class Matching (HCCM), to identify which hierarchical clusters best match individual semantic classes (e.g. male) and conjunctive semantic classes (e.g. UK & male), thereby interpreting the clusters using their matched classes. The matching degree is quantified using a new metric called the L-score, which makes imperfect matches diagnosable. HCCM's results show that hierarchical clusters analysed by SLINK are interpreted using different classes related to speaker identity, gender, and nationality, providing insight into semantics within the hierarchical organisation of our examined speaker embeddings.