Self-Supervised Learning for Speaker Recognition: A study and review

2025-11-01

Speech Communication

AI Summary

This study addresses the limitations of supervised learning in speaker recognition, which relies heavily on labeled data and exhibits poor generalization—especially under unknown conditions. The work presents a systematic investigation of self-supervised learning (SSL) for this task, offering the first comprehensive evaluation within a unified experimental framework of instance-invariance-based methods such as SimCLR, MoCo, and DINO. The analysis covers key architectural components, hyperparameter sensitivity, and in-domain/out-of-domain generalization capabilities. Results show that DINO achieves the best downstream performance by effectively modeling intra-speaker variability, albeit with high sensitivity to hyperparameters. In contrast, SimCLR and MoCo demonstrate greater robustness, excelling at capturing inter-speaker variability while avoiding representation collapse. This work elucidates the mechanistic differences among SSL approaches in modeling speaker variability and establishes a systematic benchmark and practical guidance for unsupervised speaker recognition.

Technology Category

Machine Learning: Unsupervised & Self-Supervised LearningNatural Language Processing: SpeechSearch and Optimization: Sampling/Simulation-based Search

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingUser Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systemsGraph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Problem

• Self-Supervised Learning
• Speaker Recognition
• Unlabeled Data
• Generalization
• Representation Learning

Innovation

• Self-Supervised Learning
• Speaker Recognition
• DINO
• SimCLR
• MoCo