# Discriminative Neural Clustering for Speaker Diarisation

arXiv: https://arxiv.org/abs/1910.09703 · HTML: https://mlsanity.com/paper/1910.09703/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2019.

## Built on by (most cited first, 14 of 14)

- [Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn Detection](https://mlsanity.com/paper/2109.11641/) (2021)
- [DiarizationLM: Speaker Diarization Post-Processing with Large Language Models](https://mlsanity.com/paper/2401.03506/) (2024)
- [Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors](https://mlsanity.com/paper/2107.01545/) (2021)
- [Highly Efficient Real-Time Streaming and Fully On-Device Speaker Diarization with Multi-Stage Clustering](https://mlsanity.com/paper/2210.13690/) (2022)
- [End-to-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings](https://mlsanity.com/paper/2105.02096/) (2021)
- [Improving the Naturalness of Simulated Conversations for End-to-End Neural Diarization](https://mlsanity.com/paper/2204.11232/) (2022)
- [DIVE: End-to-end Speech Diarization via Iterative Speaker Embedding](https://mlsanity.com/paper/2105.13802/) (2021)
- [Content-Aware Speaker Embeddings for Speaker Diarisation](https://mlsanity.com/paper/2102.06467/) (2021)
- [Distribution-based Emotion Recognition in Conversation](https://mlsanity.com/paper/2211.04834/) (2022)
- [CAARMA: Class Augmentation with Adversarial Mixup Regularization](https://mlsanity.com/paper/2503.16718/) (2025)
- [TOLD: A Novel Two-Stage Overlap-Aware Framework for Speaker Diarization](https://mlsanity.com/paper/2303.05397/) (2023)
- [Towards Word-Level End-to-End Neural Speaker Diarization with Auxiliary Network](https://mlsanity.com/paper/2309.08489/) (2023)
- [Speaker Recognition in the Wild](https://mlsanity.com/paper/2205.02475/) (2022)
- [From Modular to End-to-End Speaker Diarization](https://mlsanity.com/paper/2407.08752/) (2024)

## Abstract (arXiv)

In this paper, we propose Discriminative Neural Clustering (DNC) that formulates data clustering with a maximum number of clusters as a supervised sequence-to-sequence learning problem. Compared to traditional unsupervised clustering algorithms, DNC learns clustering patterns from training data without requiring an explicit definition of a similarity measure. An implementation of DNC based on the Transformer architecture is shown to be effective on a speaker diarisation task using the challenging AMI dataset. Since AMI contains only 147 complete meetings as individual input sequences, data scarcity is a significant issue for training a Transformer model for DNC. Accordingly, this paper proposes three data augmentation schemes: sub-sequence randomisation, input vector randomisation, and Diaconis augmentation, which generates new data samples by rotating the entire input sequence of L2-normalised speaker embeddings. Experimental results on AMI show that DNC achieves a reduction in speaker error rate (SER) of 29.4% relative to spectral clustering.