Self-Supervised Learning for Speech Recognition: A Comprehensive Review

Self-supervised learning (SSL) has emerged as a transformative approach in speech recognition, enabling models to leverage vast amounts of unlabelled data and reduce reliance on annotated datasets. This review systematically examines key SSL methodologies—contrastive learning, masked prediction, clustering techniques, and mutual information-based approaches—and evaluates their effectiveness in speech recognition tasks. Contrastive learning, exemplified by frameworks like SimCLR and MoCo, enhances feature robustness through data augmentation and negative sampling. Masked prediction, as demonstrated by Wav2Vec 2.0, excels at learning contextual relationships by reconstructing masked audio segments. Clustering methods improve generalization by grouping similar audio features, while mutual information-based techniques optimize representation quality. Despite their strengths, SSL methods face challenges such as implementation complexity, data quality dependence, and high computational demands. Future research directions include hybrid models combining SSL with supervised learning, multi-modal integration, and applications in low-resource languages and real-time systems. By addressing these challenges, SSL promises to advance speech recognition technologies, offering scalable and efficient solutions for diverse real-world applications.