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.

翻译：自监督学习（SSL）已成为从无标签数据中学习灵活语音表征的一种有前途的范式。通过设计利用统计规律性的前置任务，SSL模型能够捕获可用于下游任务的有用表征。本研究对Barlow Twins（BT）进行了实证分析，这是一种受人类感知中冗余降低理论启发的SSL技术。在下游任务中，BT表征加速了学习速度并可跨领域迁移。然而，BT在解耦关键解释因素方面存在局限性，仅靠冗余降低和不变性不足以将学习到的潜在变量分解为模块化、紧凑且信息丰富的编码。我们的消融研究分离了不变性约束带来的增益，但该增益依赖于具体语境。总体而言，本研究证实了Barlow Twins在样本高效语音编码中的潜力。然而，在实现完全层次化表征方面仍存在挑战。本文的分析方法和见解为引入更多归纳先验和感知原理以进一步优化BT自监督框架铺平了道路。