Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reduction
This work addresses the problem of learning efficient speech representations from unlabeled data for researchers in speech processing, but it is incremental as it builds on existing Barlow Twins methods.
This study empirically analyzed Barlow Twins, a self-supervised learning technique for speech representation, finding that it accelerated learning and transferred across domains in downstream tasks, but redundancy reduction and invariance alone were insufficient for achieving modular and compact latent codes.
Self-supervised learning (SSL) has emerged as a promising paradigm for learning flexible speech representations from unlabeled data. By designing pretext tasks that exploit statistical regularities, SSL models can capture useful representations that are transferable to downstream tasks. This study provides an empirical analysis of Barlow Twins (BT), an SSL technique inspired by 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, with redundancy reduction and invariance alone insufficient for factorization of learned latents into modular, compact, and informative codes. Our ablations study isolated gains from invariance constraints, but the gains were context-dependent. Overall, this work substantiates the potential of Barlow Twins for sample-efficient speech encoding. However, challenges remain in achieving fully hierarchical representations. The analysis methodology and insights pave a path for extensions incorporating further inductive priors and perceptual principles to further enhance the BT self-supervision framework.