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Unsupervised representation learning for speech processing has maturedgreatly in the last few years. Work in computer vision and natural languageprocessing has paved the way, but speech data offers unique challenges. As aresult, methods from other domains rarely translate directly. We review thedevelopment of unsupervised representation learning for speech over the lastdecade. We identify two primary model categories: self-supervised methods andprobabilistic latent variable models. We describe the models and develop acomprehensive taxonomy. Finally, we discuss and compare models from the twocategories.

A Brief Overview of Unsupervised Neural Speech Representation Learning