Exploring Second-Order Pattern Recognition in Speaker Recognition
In traditional pattern recognition tasks, neural networks are trained to recognise human-defined patterns (e.g. audio categories) in model inputs (e.g. audio). Meanwhile, some Explainable AI (XAI) methods explain latent patterns characterising the network's recognition of inputs as human-defined patterns; this work calls these latent patterns second-order patterns and proposes to discover them. Accordingly, we apply a hierarchical clustering algorithm to analyse whether our speaker recognition network's representations learned from known utterances naturally form hierarchical clusters. Each resulting cluster is a second-order pattern that characterises a context in our network's recogniti...
| Authors | Yanze Xu, Wenwu Wang, Mark D. Plumbley |
|---|---|
| Primary category | eess.AS |
| Published | 10 September 2026 |
| Last updated at source | 25 September 2026 |
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