# Incorporating Spatial Cues in Modular Speaker Diarization for Multi-channel Multi-party Meetings

arXiv: https://arxiv.org/abs/2409.16803 · HTML: https://mlsanity.com/paper/2409.16803/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models.
- Builds on 22 of its 34 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Placed in

- Speech and audio language models / [Speaker recognition and diarization](https://mlsanity.com/speech/speaker-recognition-and-diarization/)

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings](https://mlsanity.com/paper/2004.09249/) (2020)
- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [M2MeT: The ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription Challenge](https://mlsanity.com/paper/2110.07393/) (2021)
- [End-to-End Neural Speaker Diarization with Self-attention](https://mlsanity.com/paper/1909.06247/) (2019)
- [End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors](https://mlsanity.com/paper/2005.09921/) (2020)
- [The Third DIHARD Diarization Challenge](https://mlsanity.com/paper/2012.01477/) (2020)
- [The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios](https://mlsanity.com/paper/2306.13734/) (2023)
- [NOTSOFAR-1 Challenge: New Datasets, Baseline, and Tasks for Distant Meeting Transcription](https://mlsanity.com/paper/2401.08887/) (2024)
- [MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems](https://mlsanity.com/paper/2307.11394/) (2023)
- [3D-Speaker-Toolkit: An Open-Source Toolkit for Multimodal Speaker Verification and Diarization](https://mlsanity.com/paper/2403.19971/) (2024)
- [GPU-accelerated Guided Source Separation for Meeting Transcription](https://mlsanity.com/paper/2212.05271/) (2022)
- [The CHiME-8 DASR Challenge for Generalizable and Array Agnostic Distant Automatic Speech Recognition and Diarization](https://mlsanity.com/paper/2407.16447/) (2024)
- [Target-speaker Voice Activity Detection with Improved I-Vector Estimation for Unknown Number of Speaker](https://mlsanity.com/paper/2108.03342/) (2021)
- [Cross-Channel Attention-Based Target Speaker Voice Activity Detection: Experimental Results for M2MeT Challenge](https://mlsanity.com/paper/2202.02687/) (2022)
- [The USTC-NERCSLIP Systems for the CHiME-7 DASR Challenge](https://mlsanity.com/paper/2308.14638/) (2023)
- [The USTC-NERCSLIP Systems for the CHiME-8 NOTSOFAR-1 Challenge](https://mlsanity.com/paper/2409.02041/) (2024)
- [Neural Speaker Diarization Using Memory-Aware Multi-Speaker Embedding with Sequence-to-Sequence Architecture](https://mlsanity.com/paper/2309.09180/) (2023)
- [The CUHK-TENCENT speaker diarization system for the ICASSP 2022 multi-channel multi-party meeting transcription challenge](https://mlsanity.com/paper/2202.01986/) (2022)

## Abstract (arXiv)

Although fully end-to-end speaker diarization systems have made significant progress in recent years, modular systems often achieve superior results in real-world scenarios due to their greater adaptability and robustness. Historically, modular speaker diarization methods have seldom discussed how to leverage spatial cues from multi-channel speech. This paper proposes a three-stage modular system to enhance single-channel neural speaker diarization systems and recognition performance by utilizing spatial cues from multi-channel speech to provide more accurate initialization for each stage of neural speaker diarization (NSD) decoding: (1) Overlap detection and continuous speech separation (CSS) on multi-channel speech are used to obtain cleaner single speaker speech segments for clustering, followed by the first NSD decoding pass. (2) The results from the first pass initialize a complex Angular Central Gaussian Mixture Model (cACGMM) to estimate speaker-wise masks on multi-channel speech, and through Overlap-add and Mask-to-VAD, achieve initialization with lower speaker error (SpkErr), followed by the second NSD decoding pass. (3) The second decoding results are used for guided source separation (GSS), recognizing and filtering short segments containing less one word to obtain cleaner speech segments, followed by re-clustering and the final NSD decoding pass. We presented the progressively explored evaluation results from the CHiME-8 NOTSOFAR-1 (Natural Office Talkers in Settings Of Far-field Audio Recordings) challenge, demonstrating the effectiveness of our system and its contribution to improving recognition performance. Our final system achieved the first place in the challenge.