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

arXiv: https://arxiv.org/abs/2006.04326 · HTML: https://mlsanity.com/paper/2006.04326/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2020.

## Built on by (most cited first, 19 of 19)

- [SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation](https://mlsanity.com/paper/2209.06664/) (2022)
- [SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding](https://mlsanity.com/paper/2209.06638/) (2022)
- [Self-Supervised Speaker Verification with Simple Siamese Network and Self-Supervised Regularization](https://mlsanity.com/paper/2112.04459/) (2021)
- [Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation](https://mlsanity.com/paper/2108.03372/) (2021)
- [Learning Lip-Based Audio-Visual Speaker Embeddings with AV-HuBERT](https://mlsanity.com/paper/2205.07180/) (2022)
- [SVeritas: Benchmark for Robust Speaker Verification under Diverse Conditions](https://mlsanity.com/paper/2509.17091/) (2025)
- [Multi-Frequency Information Enhanced Channel Attention Module for Speaker Representation Learning](https://mlsanity.com/paper/2207.04540/) (2022)
- [Graph-based Label Propagation for Semi-Supervised Speaker Identification](https://mlsanity.com/paper/2106.08207/) (2021)
- [Improving Transformer-based Networks With Locality For Automatic Speaker Verification](https://mlsanity.com/paper/2302.08639/) (2023)
- [ELP-Adapters: Parameter Efficient Adapter Tuning for Various Speech Processing Tasks](https://mlsanity.com/paper/2407.21066/) (2024)
- [Semi-Supervised End-To-End Contrastive Learning For Time Series Classification](https://mlsanity.com/paper/2310.08848/) (2023)
- [Preventing Collapse in Contrastive Learning with Orthonormal Prototypes (CLOP)](https://mlsanity.com/paper/2403.18699/) (2024)
- [Multi-Level Contrastive Learning for Few-Shot Problems](https://mlsanity.com/paper/2107.07608/) (2021)
- [Pose Recognition in the Wild: Animal pose estimation using Agglomerative Clustering and Contrastive Learning](https://mlsanity.com/paper/2111.08259/) (2021)
- [Bootstrap Equilibrium and Probabilistic Speaker Representation Learning for Self-supervised Speaker Verification](https://mlsanity.com/paper/2112.08929/) (2021)
- [Hard Sample Mining Enabled Supervised Contrastive Feature Learning for Wind Turbine Pitch System Fault Diagnosis](https://mlsanity.com/paper/2306.14701/) (2023)
- [Self-supervised Reflective Learning through Self-distillation and Online Clustering for Speaker Representation Learning](https://mlsanity.com/paper/2401.01473/) (2024)
- [Semi-Supervised Contrastive Learning with Orthonormal Prototypes](https://mlsanity.com/paper/2512.07880/) (2025)
- [Self-Supervised Learning for Speaker Recognition: A study and review](https://mlsanity.com/paper/2602.10829/) (2026)

## Abstract (arXiv)

This paper introduces a semi-supervised contrastive learning framework and its application to text-independent speaker verification. The proposed framework employs generalized contrastive loss (GCL). GCL unifies losses from two different learning frameworks, supervised metric learning and unsupervised contrastive learning, and thus it naturally determines the loss for semi-supervised learning. In experiments, we applied the proposed framework to text-independent speaker verification on the VoxCeleb dataset. We demonstrate that GCL enables the learning of speaker embeddings in three manners, supervised learning, semi-supervised learning, and unsupervised learning, without any changes in the definition of the loss function.