  We present the Zero Resource Speech Challenge 2021, which asks participants
to learn a language model directly from audio, without any text or labels. The
challenge is based on the Libri-light dataset, which provides up to 60k hours
of audio from English audio books without any associated text. We provide a
pipeline baseline system consisting on an encoder based on contrastive
predictive coding (CPC), a quantizer ($k$-means) and a standard language model
(BERT or LSTM). The metrics evaluate the learned representations at the
acoustic (ABX discrimination), lexical (spot-the-word), syntactic
(acceptability judgment) and semantic levels (similarity judgment). We present
an overview of the eight submitted systems from four groups and discuss the
main results.
