  Although supervised deep learning has revolutionized speech and audio
processing, it has necessitated the building of specialist models for
individual tasks and application scenarios. It is likewise difficult to apply
this to dialects and languages for which only limited labeled data is
available. Self-supervised representation learning methods promise a single
universal model that would benefit a wide variety of tasks and domains. Such
methods have shown success in natural language processing and computer vision
domains, achieving new levels of performance while reducing the number of
labels required for many downstream scenarios. Speech representation learning
is experiencing similar progress in three main categories: generative,
contrastive, and predictive methods. Other approaches rely on multi-modal data
for pre-training, mixing text or visual data streams with speech. Although
self-supervised speech representation is still a nascent research area, it is
closely related to acoustic word embedding and learning with zero lexical
resources, both of which have seen active research for many years. This review
presents approaches for self-supervised speech representation learning and
their connection to other research areas. Since many current methods focus
solely on automatic speech recognition as a downstream task, we review recent
efforts on benchmarking learned representations to extend the application
beyond speech recognition.
