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

arXiv: https://arxiv.org/abs/2512.15229 · HTML: https://mlsanity.com/paper/2512.15229/

## 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.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [End-to-End Neural Speaker Diarization with Permutation-Free Objectives](https://mlsanity.com/paper/1909.05952/) (2019)
- [End-to-End Neural Speaker Diarization with Self-attention](https://mlsanity.com/paper/1909.06247/) (2019)
- [End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors](https://mlsanity.com/paper/2005.09921/) (2020)
- [Fully Supervised Speaker Diarization](https://mlsanity.com/paper/1810.04719/) (2018)
- [Integrating end-to-end neural and clustering-based diarization: Getting the best of both worlds](https://mlsanity.com/paper/2010.13366/) (2020)
- [Advances in integration of end-to-end neural and clustering-based diarization for real conversational speech](https://mlsanity.com/paper/2105.09040/) (2021)
- [From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization](https://mlsanity.com/paper/2204.00890/) (2022)
- [Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2107.01545/) (2021)
- [Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation](https://mlsanity.com/paper/2109.06483/) (2021)
- [Online End-to-End Neural Diarization with Speaker-Tracing Buffer](https://mlsanity.com/paper/2006.02616/) (2020)
- [Analysis of the BUT Diarization System for VoxConverse Challenge](https://mlsanity.com/paper/2010.11718/) (2020)
- [BW-EDA-EEND: Streaming End-to-End Neural Speaker Diarization for a Variable Number of Speakers](https://mlsanity.com/paper/2011.02678/) (2020)
- [Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2206.02432/) (2022)
- [The Speed Submission to DIHARD II: Contributions & Lessons Learned](https://mlsanity.com/paper/1911.02388/) (2019)
- [Robust End-to-end Speaker Diarization with Generic Neural Clustering](https://mlsanity.com/paper/2204.08164/) (2022)
- [Online speaker diarization of meetings guided by speech separation](https://mlsanity.com/paper/2402.00067/) (2024)

## 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.