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Improving Neural Diarization through Speaker Attribute Attractors and Local Dependency Modeling
David Palzer, Matthew Maciejewski, Eric Fosler-Lussier
https://arxiv.org/abs/2506.05593

Improving Neural Diarization through Speaker Attribute Attractors and Local Dependency Modeling
In recent years, end-to-end approaches have made notable progress in addressing the challenge of speaker diarization, which involves segmenting and identifying speakers in multi-talker recordings. One such approach, Encoder-Decoder Attractors (EDA), has been proposed to handle variable speaker counts as well as better guide the network during training. In this study, we extend the attractor paradigm by moving beyond direct speaker modeling and instead focus on representing more detailed `speake…