# Spatial-aware Speaker Diarization for Multi-channel Multi-party Meeting

arXiv: https://arxiv.org/abs/2209.12002 · HTML: https://mlsanity.com/paper/2209.12002/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2022.

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

- [Towards Robust Overlapping Speech Detection: A Speaker-Aware Progressive Approach Using WavLM](https://mlsanity.com/paper/2505.23207/) (2025)

## Abstract (arXiv)

This paper describes a spatial-aware speaker diarization system for the multi-channel multi-party meeting. The diarization system obtains direction information of speaker by microphone array. Speaker spatial embedding is generated by xvector and s-vector derived from superdirective beamforming (SDB) which makes the embedding more robust. Specifically, we propose a novel multi-channel sequence-to-sequence neural network architecture named discriminative multi-stream neural network (DMSNet) which consists of attention superdirective beamforming (ASDB) block and Conformer encoder. The proposed ASDB is a self-adapted channel-wise block that extracts the latent spatial features of array audios by modeling interdependencies between channels. We explore DMSNet to address overlapped speech problem on multi-channel audio and achieve 93.53% accuracy on evaluation set. By performing DMSNet based overlapped speech detection (OSD) module, the diarization error rate (DER) of cluster-based diarization system decrease significantly from 13.45% to 7.64%.