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arXiv · 2509.14737

Pushing the Limits of End-to-End Diarization

Samuel J. Broughton·Lahiru Samarakoon
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Abstract

In this paper, we present state-of-the-art diarization error rates (DERs) on multiple publicly available datasets, including AliMeeting-far, AliMeeting-near, AMI-Mix, AMI-SDM, DIHARD III, and MagicData RAMC. Leveraging EEND-TA, a single unified non-autoregressive model for end-to-end speaker diarization, we achieve new benchmark results, most notably a DER of 14.49% on DIHARD III. Our approach scales pretraining through 8-speaker simulation mixtures, ensuring each generated speaker mixture configuration is sufficiently represented. These experiments highlight that EEND-based architectures possess a greater capacity for learning than previously explored, surpassing many existing diarization solutions while maintaining efficient speeds during inference.

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Samuel J. Broughton, Lahiru Samarakoon. 2025-09-18. Pushing the Limits of End-to-End Diarization. https://arxiv.org/abs/2509.14737

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