# Multi-Channel Sequence-to-Sequence Neural Diarization: Experimental Results for The MISP 2025 Challenge

arXiv: https://arxiv.org/abs/2505.16387 · HTML: https://mlsanity.com/paper/2505.16387/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2025.

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

- [MuVAP: Multimodal Multiparty Voice Activity Projection for Turn-taking Prediction in the Wild](https://mlsanity.com/paper/2606.16731/) (2026)

## Abstract (arXiv)

This paper describes the speaker diarization system developed for the Multimodal Information-Based Speech Processing (MISP) 2025 Challenge. First, we utilize the Sequence-to-Sequence Neural Diarization (S2SND) framework to generate initial predictions using single-channel audio. Then, we extend the original S2SND framework to create a new version, Multi-Channel Sequence-to-Sequence Neural Diarization (MC-S2SND), which refines the initial results using multi-channel audio. The final system achieves a diarization error rate (DER) of 8.09% on the evaluation set of the competition database, ranking first place in the speaker diarization task of the MISP 2025 Challenge.