Interpreting hierarchical organisation of speaker embeddings
Quick summary
arXiv:2609.15203v1 Announce Type: cross 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
Key takeaways
- arXiv:2609.15203v1 Announce Type: cross 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.
Why it matters
The value of this work lies as much in how it was tested as in the claim itself. Sample design, baselines, uncertainty and replication help separate a laboratory result from real-world impact.
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