  We revisit a self-supervised method that segments unlabelled speech into
word-like segments. We start from the two-stage duration-penalised dynamic
programming method that performs zero-resource segmentation without learning an
explicit lexicon. In the first acoustic unit discovery stage, we replace
contrastive predictive coding features with HuBERT. After word segmentation in
the second stage, we get an acoustic word embedding for each segment by
averaging HuBERT features. These embeddings are clustered using K-means to get
a lexicon. The result is good full-coverage segmentation with a lexicon that
achieves state-of-the-art performance on the ZeroSpeech benchmarks.
