# DiaPer: End-to-End Neural Diarization with Perceiver-Based Attractors

arXiv: https://arxiv.org/abs/2312.04324 · HTML: https://mlsanity.com/paper/2312.04324/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2023.

## Built on by (most cited first, 6 of 6)

- [End-to-End Supervised Hierarchical Graph Clustering for Speaker Diarization](https://mlsanity.com/paper/2401.12850/) (2024)
- [Speakers Unembedded: Embedding-free Approach to Long-form Neural Diarization](https://mlsanity.com/paper/2406.18679/) (2024)
- [Probabilistic Fusion and Calibration of Neural Speaker Diarization Models](https://mlsanity.com/paper/2511.22696/) (2025)
- [From Modular to End-to-End Speaker Diarization](https://mlsanity.com/paper/2407.08752/) (2024)
- [Speaker Diarization with Overlapping Community Detection Using Graph Attention Networks and Label Propagation Algorithm](https://mlsanity.com/paper/2506.02610/) (2025)
- [Neural Speaker Diarization via Multilingual Training: Evaluation on Low-Resource Nepali-Hindi Speech](https://mlsanity.com/paper/2606.26144/) (2026)

## Abstract (arXiv)

Until recently, the field of speaker diarization was dominated by cascaded systems. Due to their limitations, mainly regarding overlapped speech and cumbersome pipelines, end-to-end models have gained great popularity lately. One of the most successful models is end-to-end neural diarization with encoder-decoder based attractors (EEND-EDA). In this work, we replace the EDA module with a Perceiver-based one and show its advantages over EEND-EDA; namely obtaining better performance on the largely studied Callhome dataset, finding the quantity of speakers in a conversation more accurately, and faster inference time. Furthermore, when exhaustively compared with other methods, our model, DiaPer, reaches remarkable performance with a very lightweight design. Besides, we perform comparisons with other works and a cascaded baseline across more than ten public wide-band datasets. Together with this publication, we release the code of DiaPer as well as models trained on public and free data.