  Recent progress in self-supervised or unsupervised machine learning has
opened the possibility of building a full speech processing system from raw
audio without using any textual representations or expert labels such as
phonemes, dictionaries or parse trees. The contribution of the Zero Resource
Speech Challenge series since 2015 has been to break down this long-term
objective into four well-defined tasks -- Acoustic Unit Discovery, Spoken Term
Discovery, Discrete Resynthesis, and Spoken Language Modeling -- and introduce
associated metrics and benchmarks enabling model comparison and cumulative
progress. We present an overview of the six editions of this challenge series
since 2015, discuss the lessons learned, and outline the areas which need more
work or give puzzling results.
