# Assessing the Robustness of Spectral Clustering for Deep Speaker Diarization

arXiv: https://arxiv.org/abs/2403.14286 · HTML: https://mlsanity.com/paper/2403.14286/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 40 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [Speech Enhancement and Dereverberation with Diffusion-based Generative Models](https://mlsanity.com/paper/2208.05830/) (2022)
- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [Speaker Diarization with LSTM](https://mlsanity.com/paper/1710.10468/) (2017)
- [Speaker Recognition Based on Deep Learning: An Overview](https://mlsanity.com/paper/2012.00931/) (2020)
- [The Third DIHARD Diarization Challenge](https://mlsanity.com/paper/2012.01477/) (2020)
- [ECAPA-TDNN Embeddings for Speaker Diarization](https://mlsanity.com/paper/2104.01466/) (2021)
- [Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap](https://mlsanity.com/paper/2003.02405/) (2020)
- [LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization](https://mlsanity.com/paper/1907.10393/) (2019)
- [EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion Recognition](https://mlsanity.com/paper/2103.08310/) (2021)
- [DiarizationLM: Speaker Diarization Post-Processing with Large Language Models](https://mlsanity.com/paper/2401.03506/) (2024)
- [Third DIHARD Challenge Evaluation Plan](https://mlsanity.com/paper/2006.05815/) (2020)
- [Cross-Corpora Spoken Language Identification with Domain Diversification and Generalization](https://mlsanity.com/paper/2302.05110/) (2023)
- [Separation Guided Speaker Diarization in Realistic Mismatched Conditions](https://mlsanity.com/paper/2107.02357/) (2021)

## Built on by (most cited first, 2 of 2)

- [Self-Tuning Spectral Clustering for Speaker Diarization](https://mlsanity.com/paper/2410.00023/) (2024)
- [TCG CREST System Description for the DISPLACE-M Challenge](https://mlsanity.com/paper/2603.02030/) (2026)

## Abstract (arXiv)

Clustering speaker embeddings is crucial in speaker diarization but hasn't received as much focus as other components. Moreover, the robustness of speaker diarization across various datasets hasn't been explored when the development and evaluation data are from different domains. To bridge this gap, this study thoroughly examines spectral clustering for both same-domain and cross-domain speaker diarization. Our extensive experiments on two widely used corpora, AMI and DIHARD, reveal the performance trend of speaker diarization in the presence of domain mismatch. We observe that the performance difference between two different domain conditions can be attributed to the role of spectral clustering. In particular, keeping other modules unchanged, we show that differences in optimal tuning parameters as well as speaker count estimation originates due to the mismatch. This study opens several future directions for speaker diarization research.