Explainable AI in Speaker Recognition -- Making Latent Representations Understandable 文章

arXiv:2604.23354v2 Announce Type: replace-cross Abstract: Neural networks can be trained to learn task-relevant representations from data. Understanding how these networks make decisions falls within the Explainable AI (XAI) domain. This paper proposes to study an XAI topic: uncovering the unknown organisation in the representations, particularly those a speaker recognition network learns from utterances, for recognising speaker identity. Past studies have employed algorithms (e.g. K-means) to analyse how network representations can be naturally organised into independent clusters in different ways, i.e., to analyse flat clustering phenomena within the space defined by these representations, referred to as the network representation space.