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Self-supervised learning (SSL) has emerged as a promising paradigm forlearning flexible speech representations from unlabeled data. By designingpretext tasks that exploit statistical regularities, SSL models can captureuseful representations that are transferable to downstream tasks. This studyprovides an empirical analysis of Barlow Twins (BT), an SSL technique inspiredby theories of redundancy reduction in human perception. On downstream tasks,BT representations accelerated learning and transferred across domains.However, limitations exist in disentangling key explanatory factors, withredundancy reduction and invariance alone insufficient for factorization oflearned latents into modular, compact, and informative codes. Our ablationsstudy isolated gains from invariance constraints, but the gains werecontext-dependent. Overall, this work substantiates the potential of BarlowTwins for sample-efficient speech encoding. However, challenges remain inachieving fully hierarchical representations. The analysis methodology andinsights pave a path for extensions incorporating further inductive priors andperceptual principles to further enhance the BT self-supervision framework.

Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction