# Progressive Sub-Graph Clustering Algorithm for Semi-Supervised Domain Adaptation Speaker Verification

arXiv: https://arxiv.org/abs/2305.12703 · HTML: https://mlsanity.com/paper/2305.12703/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 38 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [A Survey of Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1812.02849/) (2018)
- [Correlation Alignment for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1612.01939/) (2016)
- [Contextual Transformer Networks for Visual Recognition](https://mlsanity.com/paper/2107.12292/) (2021)
- [CN-CELEB: a challenging Chinese speaker recognition dataset](https://mlsanity.com/paper/1911.01799/) (2019)
- [Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition System](https://mlsanity.com/paper/1804.05160/) (2018)
- [MFA-Conformer: Multi-scale Feature Aggregation Conformer for Automatic Speaker Verification](https://mlsanity.com/paper/2203.15249/) (2022)
- [CN-Celeb: multi-genre speaker recognition](https://mlsanity.com/paper/2012.12468/) (2020)
- [The IDLAB VoxCeleb Speaker Recognition Challenge 2020 System Description](https://mlsanity.com/paper/2010.12468/) (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)
- [Multi-query multi-head attention pooling and Inter-topK penalty for speaker verification](https://mlsanity.com/paper/2110.05042/) (2021)
- [Cross-lingual Text-independent Speaker Verification using Unsupervised Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1908.01447/) (2019)
- [An iterative framework for self-supervised deep speaker representation learning](https://mlsanity.com/paper/2010.14751/) (2020)
- [PCF: ECAPA-TDNN with Progressive Channel Fusion for Speaker Verification](https://mlsanity.com/paper/2303.00204/) (2023)
- [SJTU-AISPEECH System for VoxCeleb Speaker Recognition Challenge 2022](https://mlsanity.com/paper/2209.09076/) (2022)
- [NIST SRE CTS Superset: A large-scale dataset for telephony speaker recognition](https://mlsanity.com/paper/2108.07118/) (2021)
- [Speaker verification using end-to-end adversarial language adaptation](https://mlsanity.com/paper/1811.02331/) (2018)
- [The CORAL++ Algorithm for Unsupervised Domain Adaptation of Speaker Recogntion](https://mlsanity.com/paper/2202.01092/) (2022)
- [The DKU-Tencent System for the VoxCeleb Speaker Recognition Challenge 2022](https://mlsanity.com/paper/2210.05092/) (2022)
- [Channel adversarial training for cross-channel text-independent speaker recognition](https://mlsanity.com/paper/1902.09074/) (2019)
- [The Phonexia VoxCeleb Speaker Recognition Challenge 2021 System Description](https://mlsanity.com/paper/2109.02052/) (2021)

## Abstract (arXiv)

Utilizing the large-scale unlabeled data from the target domain via pseudo-label clustering algorithms is an important approach for addressing domain adaptation problems in speaker verification tasks. In this paper, we propose a novel progressive subgraph clustering algorithm based on multi-model voting and double-Gaussian based assessment (PGMVG clustering). To fully exploit the relationships among utterances and the complementarity among multiple models, our method constructs multiple k-nearest neighbors graphs based on diverse models and generates high-confidence edges using a voting mechanism. Further, to maximize the intra-class diversity, the connected subgraph is utilized to obtain the initial pseudo-labels. Finally, to prevent disastrous clustering results, we adopt an iterative approach that progressively increases k and employs a double-Gaussian based assessment algorithm to decide whether merging sub-classes.