# Toward accurate speaker inventories for online speaker diarization

arXiv: https://arxiv.org/abs/2610.11278 · HTML: https://mlsanity.com/paper/2610.11278/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models.
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 8 Oct 2026.

## Placed in

- Speech and audio language models / [Speaker recognition and diarization](https://mlsanity.com/speech/speaker-recognition-and-diarization/)

## Builds on (most cited first, 15 of 15)

- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [M2MeT: The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Challenge](https://mlsanity.com/paper/2110.07393/) (2021)
- [Spot the conversation: speaker diarisation in the wild](https://mlsanity.com/paper/2007.01216/) (2020)
- [The Second DIHARD Diarization Challenge: Dataset, task, and baselines](https://mlsanity.com/paper/1906.07839/) (2019)
- [Online End-to-End Neural Diarization with Speaker-Tracing Buffer](https://mlsanity.com/paper/2006.02616/) (2020)
- [Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation](https://mlsanity.com/paper/2109.06483/) (2021)
- [FireRedASR2S: A State-of-the-Art Industrial-Grade All-in-One Automatic Speech Recognition System](https://mlsanity.com/paper/2603.10420/) (2026)
- [Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2206.02432/) (2022)
- [Streaming Sortformer: Speaker Cache-Based Online Speaker Diarization with Arrival-Time Ordering](https://mlsanity.com/paper/2507.18446/) (2025)
- [The VoxCeleb Speaker Recognition Challenge: A Retrospective](https://mlsanity.com/paper/2408.14886/) (2024)
- [Sequence-to-Sequence Neural Diarization with Automatic Speaker Detection and Representation](https://mlsanity.com/paper/2411.13849/) (2024)
- [Low-Latency Online Speaker Diarization with Graph-Based Label Generation](https://mlsanity.com/paper/2111.13803/) (2021)
- [ReDimNet2: Scaling Speaker Verification via Time-Pooled Dimension Reshaping](https://mlsanity.com/paper/2603.11841/) (2026)
- [Absolute decision corrupts absolutely: conservative online speaker diarisation](https://mlsanity.com/paper/2211.04768/) (2022)
- [LS-EEND: Long-Form Streaming End-to-End Neural Diarization with Online Attractor Extraction](https://mlsanity.com/paper/2410.06670/) (2024)

## Abstract (arXiv)

An online speaker diarizer maintains a growing inventory of speakers, and applications read it directly: a live transcript enumerates them as they appear. Existing systems either fix the inventory's size in advance, so speakers beyond that size are merged, or grow it on distance alone, so noise and turn boundaries split speakers. Scored on speaker count over nine public datasets, both failures appear where DER reports neither: on VoxSRC-23 the fixed-capacity systems return 44-46% too few speakers and the unbounded tracker with the lowest DER 63% too many. We build on that embedding-based tracker and change only what triggers a registration. A candidate speaker pool keeps unregistered embeddings apart and registers a speaker only once one candidate has gathered enough mutually compatible embeddings; commit-gated label assignment holds an output while its candidate is undecided, so no speaker appears in the output before it is confirmed. Across the nine datasets the macro speaker-count error falls from 225% to 21% of the reference and macro DER from 17.59% to 15.95%, at a mean added latency of 0.109s over all outputs; on VoxSRC-23 the tracker obtains both the lowest DER and the most accurate speaker count of the systems compared.