# A Review of Common Online Speaker Diarization Methods

arXiv: https://arxiv.org/abs/2406.14464 · HTML: https://mlsanity.com/paper/2406.14464/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 20 Jun 2024.

## Builds on (most cited first, 19 of 19)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [Spot the conversation: speaker diarisation in the wild](https://mlsanity.com/paper/2007.01216/) (2020)
- [Continuous speech separation: dataset and analysis](https://mlsanity.com/paper/2001.11482/) (2020)
- [Speaker Diarization with LSTM](https://mlsanity.com/paper/1710.10468/) (2017)
- [The Third DIHARD Diarization Challenge](https://mlsanity.com/paper/2012.01477/) (2020)
- [Fully Supervised Speaker Diarization](https://mlsanity.com/paper/1810.04719/) (2018)
- [The Second DIHARD Diarization Challenge: Dataset, task, and baselines](https://mlsanity.com/paper/1906.07839/) (2019)
- [Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn Detection](https://mlsanity.com/paper/2109.11641/) (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)
- [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)
- [Speaker Diarization as a Fully Online Learning Problem in MiniVox](https://mlsanity.com/paper/2006.04376/) (2020)
- [Low-Latency Speech Separation Guided Diarization for Telephone Conversations](https://mlsanity.com/paper/2204.02306/) (2022)
- [Supervised online diarization with sample mean loss for multi-domain data](https://mlsanity.com/paper/1911.01266/) (2019)
- [Low-Latency Online Speaker Diarization with Graph-Based Label Generation](https://mlsanity.com/paper/2111.13803/) (2021)
- [Frame-wise streaming end-to-end speaker diarization with non-autoregressive self-attention-based attractors](https://mlsanity.com/paper/2309.13916/) (2023)
- [End-to-end Online Speaker Diarization with Target Speaker Tracking](https://mlsanity.com/paper/2310.08696/) (2023)

## Abstract (arXiv)

Speaker diarization provides the answer to the question "who spoke when?" for an audio file. This information can be used to complete audio transcripts for further processing steps. Most speaker diarization systems assume that the audio file is available as a whole. However, there are scenarios in which the speaker labels are needed immediately after the arrival of an audio segment. Speaker diarization with a correspondingly low latency is referred to as online speaker diarization. This paper provides an overview. First the history of online speaker diarization is briefly presented. Next a taxonomy and datasets for training and evaluation are given. In the sections that follow, online diarization methods and systems are discussed in detail. This paper concludes with the presentation of challenges that still need to be solved by future research in the field of online speaker diarization.