# Semi-Supervised Contrastive Learning with Generalized Contrastive Loss and Its Application to Speaker Recognition

**Authors:** Nakamasa Inoue, Keita Goto  
**arXiv:** [2006.04326](https://arxiv.org/abs/2006.04326)  
**Date:** 2020-06-08  
**Summary page:** https://www.summarizepaper.com/en/paper/2006.04326v1/

## Key points

- Novel semi-supervised contrastive learning framework for text-independent speaker verification
- Utilizes a generalized contrastive loss (GCL) combining supervised and unsupervised losses
- Enables semi-supervised learning by determining loss naturally
- Applied to text-independent speaker verification using VoxCeleb dataset
- GCL enables learning of speaker embeddings in three ways: supervised, semi-supervised, and unsupervised learning
- Advances speaker recognition systems by leveraging labeled and unlabeled data effectively

## Summary

This paper presents a novel semi-supervised contrastive learning framework for text-independent speaker verification. The framework utilizes a generalized contrastive loss (GCL) that combines losses from supervised metric learning and unsupervised contrastive learning. This allows the framework to naturally determine the loss for semi-supervised learning. The authors applied the proposed framework to text-independent speaker verification using the VoxCeleb dataset and demonstrated that GCL enables the learning of speaker embeddings in three different ways: supervised learning, semi-supervised learning, and unsupervised learning without any changes to the definition of the loss function. Importantly, this work contributes to advancing speaker recognition systems by providing a flexible and effective approach for leveraging both labeled and unlabeled data in training models.