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

TL;DR

This paper empirically analyzes Barlow Twins, a self-supervised learning method for speech, highlighting its benefits in transferability and sample efficiency, while discussing its limitations in disentangling learned representations.

Contribution

It provides an empirical evaluation of Barlow Twins for speech, revealing its strengths and limitations, and suggests directions for incorporating additional priors to improve hierarchical representations.

Findings

Barlow Twins accelerates learning in downstream speech tasks.

Representations transfer effectively across domains.

Redundancy reduction alone is insufficient for full factorization.

Abstract

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.