O-EENC-SD: Efficient Online End-to-End Neural Clustering for Speaker Diarization

• URL: http://arxiv.org/abs/2512.15229v1
• Date: Wed, 17 Dec 2025 09:27:23 GMT
• Title: O-EENC-SD: Efficient Online End-to-End Neural Clustering for Speaker Diarization
• Authors: Elio Gruttadauria, Mathieu Fontaine, Jonathan Le Roux, Slim Essid,
• Abstract summary: O-EENC-SD is an end-to-end online speaker diarization system based on EEND-EDA.<n>We develop a novel centroid refinement decoder whose usefulness is assessed through a rigorous ablation study.
• Score: 46.665605221223295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce O-EENC-SD: an end-to-end online speaker diarization system based on EEND-EDA, featuring a novel RNN-based stitching mechanism for online prediction. In particular, we develop a novel centroid refinement decoder whose usefulness is assessed through a rigorous ablation study. Our system provides key advantages over existing methods: a hyperparameter-free solution compared to unsupervised clustering approaches, and a more efficient alternative to current online end-to-end methods, which are computationally costly. We demonstrate that O-EENC-SD is competitive with the state of the art in the two-speaker conversational telephone speech domain, as tested on the CallHome dataset. Our results show that O-EENC-SD provides a great trade-off between DER and complexity, even when working on independent chunks with no overlap, making the system extremely efficient.

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