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cs.SD2025
End-to-End Diarization utilizing Attractor Deep Clustering
David Palzer, Matthew Maciejewski, Eric Fosler-Lussier
Speaker diarization remains challenging due to the need for structured speaker representations, efficient modeling, and robustness to varying conditions. We propose a performant, c…
cs.SD2025
Improving Neural Diarization through Speaker Attribute Attractors and Local Dependency Modeling
David Palzer, Matthew Maciejewski, Eric Fosler-Lussier
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-ta…