# LSTM based Similarity Measurement with Spectral Clustering for Speaker Diarization

arXiv: https://arxiv.org/abs/1907.10393 · HTML: https://mlsanity.com/paper/1907.10393/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2019.

## Built on by (most cited first, 17 of 17)

- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [A Large-Scale Evaluation of Speech Foundation Models](https://mlsanity.com/paper/2404.09385/) (2024)
- [The VoxCeleb Speaker Recognition Challenge: A Retrospective](https://mlsanity.com/paper/2408.14886/) (2024)
- [Supervised Hierarchical Clustering using Graph Neural Networks for Speaker Diarization](https://mlsanity.com/paper/2302.12716/) (2023)
- [A Benchmark for Multi-speaker Anonymization](https://mlsanity.com/paper/2407.05608/) (2024)
- [Revisiting Active Speaker Detection: An In-the-Wild Benchmark for Generalization and Robustness](https://mlsanity.com/paper/2505.21954/) (2025)
- [DIVE: End-to-end Speech Diarization via Iterative Speaker Embedding](https://mlsanity.com/paper/2105.13802/) (2021)
- [Summary of the DISPLACE Challenge 2023 - DIarization of SPeaker and LAnguage in Conversational Environments](https://mlsanity.com/paper/2311.12564/) (2023)
- [Self-supervised Speaker Diarization](https://mlsanity.com/paper/2204.04166/) (2022)
- [Reformulating Speaker Diarization as Community Detection With Emphasis On Topological Structure](https://mlsanity.com/paper/2204.12112/) (2022)
- [Self-Tuning Spectral Clustering for Speaker Diarization](https://mlsanity.com/paper/2410.00023/) (2024)
- [Assessing the Robustness of Spectral Clustering for Deep Speaker Diarization](https://mlsanity.com/paper/2403.14286/) (2024)
- [TSUP Speaker Diarization System for Conversational Short-phrase Speaker Diarization Challenge](https://mlsanity.com/paper/2210.14653/) (2022)
- [DS-TDNN: Dual-stream Time-delay Neural Network with Global-aware Filter for Speaker Verification](https://mlsanity.com/paper/2303.11020/) (2023)
- [End-to-End Supervised Hierarchical Graph Clustering for Speaker Diarization](https://mlsanity.com/paper/2401.12850/) (2024)
- [Cross-stitched Multi-modal Encoders](https://mlsanity.com/paper/2204.09227/) (2022)
- [Bi-LSTM Scoring Based Similarity Measurement with Agglomerative Hierarchical Clustering (AHC) for Speaker Diarization](https://mlsanity.com/paper/2205.09709/) (2022)

## Abstract (arXiv)

More and more neural network approaches have achieved considerable improvement upon submodules of speaker diarization system, including speaker change detection and segment-wise speaker embedding extraction. Still, in the clustering stage, traditional algorithms like probabilistic linear discriminant analysis (PLDA) are widely used for scoring the similarity between two speech segments. In this paper, we propose a supervised method to measure the similarity matrix between all segments of an audio recording with sequential bidirectional long short-term memory networks (Bi-LSTM). Spectral clustering is applied on top of the similarity matrix to further improve the performance. Experimental results show that our system significantly outperforms the state-of-the-art methods and achieves a diarization error rate of 6.63% on the NIST SRE 2000 CALLHOME database.