  We present a number of low-resource approaches to the tasks of the Zero
Resource Speech Challenge 2021. We build on the unsupervised representations of
speech proposed by the organizers as a baseline, derived from CPC and clustered
with the k-means algorithm. We demonstrate that simple methods of refining
those representations can narrow the gap, or even improve upon the solutions
which use a high computational budget. The results lead to the conclusion that
the CPC-derived representations are still too noisy for training language
models, but stable enough for simpler forms of pattern matching and retrieval.
