  Unsupervised representation learning for speech processing has matured
greatly in the last few years. Work in computer vision and natural language
processing has paved the way, but speech data offers unique challenges. As a
result, methods from other domains rarely translate directly. We review the
development of unsupervised representation learning for speech over the last
decade. We identify two primary model categories: self-supervised methods and
probabilistic latent variable models. We describe the models and develop a
comprehensive taxonomy. Finally, we discuss and compare models from the two
categories.
