זיהוי דפוסים מסדר שני בזיהוי דוברים

תקציר מקורי באנגליתarXiv:2609.11182v1 Announce Type: cross Abstract: In classical pattern recognition tasks, neural networks are trained to recognise human-defined patterns for model inputs. Some Explainable AI (XAI) methods can explain other latent patterns that underlie the network's recognition of inputs as human-defined patterns; in this work, we call these latent patterns second-order patterns, and we propose to discover them. To this end, we apply a hierarchical clustering algorithm to analyse whether representations learned by a speaker recognition network from utterances naturally form hierarchical clusters. Each resulting cluster represents a second-order pattern that characterises how the network recognises some known utterances as speaker identities. All the resulting second-order patterns are the