# Systematic Evaluation of Online Speaker Diarization Systems Regarding their Latency

arXiv: https://arxiv.org/abs/2407.04293 · HTML: https://mlsanity.com/paper/2407.04293/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 18 references that are in the map.
- First posted on arXiv 5 Jul 2024.

## Builds on (most cited first, 10 of 10)

- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [Wespeaker: A Research and Production oriented Speaker Embedding Learning Toolkit](https://mlsanity.com/paper/2210.17016/) (2022)
- [End-to-end speaker segmentation for overlap-aware resegmentation](https://mlsanity.com/paper/2104.04045/) (2021)
- [Spot the conversation: speaker diarisation in the wild](https://mlsanity.com/paper/2007.01216/) (2020)
- [Fully Supervised Speaker Diarization](https://mlsanity.com/paper/1810.04719/) (2018)
- [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)
- [Supervised online diarization with sample mean loss for multi-domain data](https://mlsanity.com/paper/1911.01266/) (2019)
- [Frame-wise streaming end-to-end speaker diarization with non-autoregressive self-attention-based attractors](https://mlsanity.com/paper/2309.13916/) (2023)

## Built on by (most cited first, 1 of 1)

- [Benchmarking Diarization Models](https://mlsanity.com/paper/2509.26177/) (2025)

## Abstract (arXiv)

In this paper, different online speaker diarization systems are evaluated on the same hardware with the same test data with regard to their latency. The latency is the time span from audio input to the output of the corresponding speaker label. As part of the evaluation, various model combinations within the DIART framework, a diarization system based on the online clustering algorithm UIS-RNN-SML, and the end-to-end online diarization system FS-EEND are compared. The lowest latency is achieved for the DIART-pipeline with the embedding model pyannote/embedding and the segmentation model pyannote/segmentation. The FS-EEND system shows a similarly good latency. In general there is currently no published research that compares several online diarization systems in terms of their latency. This makes this work even more relevant.