# Adapting Speaker Embeddings for Speaker Diarisation

arXiv: https://arxiv.org/abs/2104.02879 · HTML: https://mlsanity.com/paper/2104.02879/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 39 references that are in the map.
- First posted on arXiv 7 Apr 2021.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [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)
- [Long Short-Term Memory-Networks for Machine Reading](https://mlsanity.com/paper/1601.06733/) (2016)
- [CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings](https://mlsanity.com/paper/2004.09249/) (2020)
- [A Light CNN for Deep Face Representation with Noisy Labels](https://mlsanity.com/paper/1511.02683/) (2015)
- [In defence of metric learning for speaker recognition](https://mlsanity.com/paper/2003.11982/) (2020)
- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [Spot the conversation: speaker diarisation in the wild](https://mlsanity.com/paper/2007.01216/) (2020)
- [Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario](https://mlsanity.com/paper/2005.07272/) (2020)
- [The Third DIHARD Diarization Challenge](https://mlsanity.com/paper/2012.01477/) (2020)
- [The Second DIHARD Diarization Challenge: Dataset, task, and baselines](https://mlsanity.com/paper/1906.07839/) (2019)
- [Clova Baseline System for the VoxCeleb Speaker Recognition Challenge 2020](https://mlsanity.com/paper/2009.14153/) (2020)
- [The ins and outs of speaker recognition: lessons from VoxSRC 2020](https://mlsanity.com/paper/2010.15809/) (2020)
- [End-to-End Speaker Diarization as Post-Processing](https://mlsanity.com/paper/2012.10055/) (2020)
- [Analysis of the BUT Diarization System for VoxConverse Challenge](https://mlsanity.com/paper/2010.11718/) (2020)
- [The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap](https://mlsanity.com/paper/2102.01363/) (2021)
- [Look who's not talking](https://mlsanity.com/paper/2011.14885/) (2020)
- [BUT System Description for DIHARD Speech Diarization Challenge 2019](https://mlsanity.com/paper/1910.08847/) (2019)
- [Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers](https://mlsanity.com/paper/2101.08473/) (2021)

## Built on by (most cited first, 4 of 4)

- [Multi-scale speaker embedding-based graph attention networks for speaker diarisation](https://mlsanity.com/paper/2110.03361/) (2021)
- [In search of strong embedding extractors for speaker diarisation](https://mlsanity.com/paper/2210.14682/) (2022)
- [High-resolution embedding extractor for speaker diarisation](https://mlsanity.com/paper/2211.04060/) (2022)
- [Advancing the dimensionality reduction of speaker embeddings for speaker diarisation: disentangling noise and informing speech activity](https://mlsanity.com/paper/2110.03380/) (2021)

## Abstract (arXiv)

The goal of this paper is to adapt speaker embeddings for solving the problem of speaker diarisation. The quality of speaker embeddings is paramount to the performance of speaker diarisation systems. Despite this, prior works in the field have directly used embeddings designed only to be effective on the speaker verification task. In this paper, we propose three techniques that can be used to better adapt the speaker embeddings for diarisation: dimensionality reduction, attention-based embedding aggregation, and non-speech clustering. A wide range of experiments is performed on various challenging datasets. The results demonstrate that all three techniques contribute positively to the performance of the diarisation system achieving an average relative improvement of 25.07% in terms of diarisation error rate over the baseline.