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

arXiv 2512.15229 ↗First posted 17 Dec 2025cs.LG cs.SD eess.SPcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 26 references that are in the map. First posted on arXiv 17 Dec 2025.

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References · 20

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  3. 2019Transformer-XL: Attentive Language Models Beyond a Fixed-Length Contextcited by 1,850
  4. 2021A Review of Speaker Diarization: Recent Advances with Deep Learningcited by 124
  5. 2019End-to-End Neural Speaker Diarization with Permutation-Free Objectivescited by 88
  6. 2019End-to-End Neural Speaker Diarization with Self-attentioncited by 77
  7. 2020End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractorscited by 71
  8. 2018Fully Supervised Speaker Diarizationcited by 47
  9. 2020Integrating end-to-end neural and clustering-based diarization: Getting the best of both worldscited by 35
  10. 2021Advances in integration of end-to-end neural and clustering-based diarization for real conversational speechcited by 23
  11. 2022From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarizationcited by 20
  12. 2021Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractorscited by 15
8 more
  1. 2021Overlap-aware low-latency online speaker diarization based on end-to-end local segmentationcited by 15
  2. 2020Online End-to-End Neural Diarization with Speaker-Tracing Buffercited by 14
  3. 2020Analysis of the BUT Diarization System for VoxConverse Challengecited by 11
  4. 2020BW-EDA-EEND: Streaming End-to-End Neural Speaker Diarization for a Variable Number of Speakerscited by 11
  5. 2022Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractorscited by 8
  6. 2019The Speed Submission to DIHARD II: Contributions & Lessons Learnedcited by 3
  7. 2022Robust End-to-end Speaker Diarization with Generic Neural Clusteringcited by 2
  8. 2024Online speaker diarization of meetings guided by speech separationcited by 2

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