# Graph Convolutional Network Based Semi-Supervised Learning on Multi-Speaker Meeting Data

arXiv: https://arxiv.org/abs/2204.11501 · HTML: https://mlsanity.com/paper/2204.11501/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2022.

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

- [Supervised Hierarchical Clustering using Graph Neural Networks for Speaker Diarization](https://mlsanity.com/paper/2302.12716/) (2023)
- [End-to-End Supervised Hierarchical Graph Clustering for Speaker Diarization](https://mlsanity.com/paper/2401.12850/) (2024)
- [Low resource cross-modal alignment using HGNN to enhance speech representation](https://mlsanity.com/paper/2609.23191/) (2026)
- [Enhancing speech representation learning with cross-modal knowledge transfer with HGNN under low resource settings: the case study of Yemba](https://mlsanity.com/paper/2609.23194/) (2026)

## Abstract (arXiv)

Unsupervised clustering on speakers is becoming increasingly important for its potential uses in semi-supervised learning. In reality, we are often presented with enormous amounts of unlabeled data from multi-party meetings and discussions. An effective unsupervised clustering approach would allow us to significantly increase the amount of training data without additional costs for annotations. Recently, methods based on graph convolutional networks (GCN) have received growing attention for unsupervised clustering, as these methods exploit the connectivity patterns between nodes to improve learning performance. In this work, we present a GCN-based approach for semi-supervised learning. Given a pre-trained embedding extractor, a graph convolutional network is trained on the labeled data and clusters unlabeled data with "pseudo-labels". We present a self-correcting training mechanism that iteratively runs the cluster-train-correct process on pseudo-labels. We show that this proposed approach effectively uses unlabeled data and improves speaker recognition accuracy.