# Multi-objective Progressive Clustering for Semi-supervised Domain Adaptation in Speaker Verification

arXiv: https://arxiv.org/abs/2310.04760 · HTML: https://mlsanity.com/paper/2310.04760/

## Key facts

- Cited by 1 papers in the map (data through 19 Oct 2026).
- Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models.
- Builds on 18 of its 26 references that are in the map.
- First posted on arXiv 7 Oct 2023.

## Placed in

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

## Builds on (most cited first, 18 of 18)

- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018)
- [Attentive Statistics Pooling for Deep Speaker Embedding](https://mlsanity.com/paper/1803.10963/) (2018)
- [Correlation Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1612.01939/) (2016)
- [Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System](https://mlsanity.com/paper/1804.05160/) (2018)
- [CN-Celeb: multi-genre speaker recognition](https://mlsanity.com/paper/2012.12468/) (2020)
- [Simple Attention Module based Speaker Verification with Iterative noisy label detection](https://mlsanity.com/paper/2110.06534/) (2021)
- [The DKU-DukeECE Systems for VoxCeleb Speaker Recognition Challenge 2020](https://mlsanity.com/paper/2010.12731/) (2020)
- [The CORAL+ Algorithm for Unsupervised Domain Adaptation of PLDA](https://mlsanity.com/paper/1812.10260/) (2018)
- [Self-Supervised Learning Based Domain Adaptation for Robust Speaker Verification](https://mlsanity.com/paper/2108.13843/) (2021)
- [Speaker verification using end-to-end adversarial language adaptation](https://mlsanity.com/paper/1811.02331/) (2018)
- [Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-End Speaker Verification](https://mlsanity.com/paper/1811.03063/) (2018)
- [The DKU-MSXF Speaker Verification System for the VoxCeleb Speaker Recognition Challenge 2023](https://mlsanity.com/paper/2308.08766/) (2023)
- [The DKU-Tencent System for the VoxCeleb Speaker Recognition Challenge 2022](https://mlsanity.com/paper/2210.05092/) (2022)
- [Cross-domain Adaptation with Discrepancy Minimization for Text-independent Forensic Speaker Verification](https://mlsanity.com/paper/2009.02444/) (2020)
- [The 2022 Far-field Speaker Verification Challenge: Exploring domain mismatch and semi-supervised learning under the far-field scenario](https://mlsanity.com/paper/2209.05273/) (2022)
- [The HCCL system for VoxCeleb Speaker Recognition Challenge 2022](https://mlsanity.com/paper/2305.12642/) (2023)

## Built on by (most cited first, 1 of 1)

- [Overview of Speaker Modeling and Its Applications: From the Lens of Deep Speaker Representation Learning](https://mlsanity.com/paper/2407.15188/) (2024)

## Abstract (arXiv)

Utilizing the pseudo-labeling algorithm with large-scale unlabeled data becomes crucial for semi-supervised domain adaptation in speaker verification tasks. In this paper, we propose a novel pseudo-labeling method named Multi-objective Progressive Clustering (MoPC), specifically designed for semi-supervised domain adaptation. Firstly, we utilize limited labeled data from the target domain to derive domain-specific descriptors based on multiple distinct objectives, namely within-graph denoising, intra-class denoising and inter-class denoising. Then, the Infomap algorithm is adopted for embedding clustering, and the descriptors are leveraged to further refine the target domain's pseudo-labels. Moreover, to further improve the quality of pseudo labels, we introduce the subcenter-purification and progressive-merging strategy for label denoising. Our proposed MoPC method achieves 4.95% EER and ranked the 1st place on the evaluation set of VoxSRC 2023 track 3. We also conduct additional experiments on the FFSVC dataset and yield promising results.