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.

我们重新审视了一种自我监督的方法，将未标记的语音分割成类似词的段落。我们从两阶段的惩罚持续时间的动态规划方法开始，进行零资源分割，而无需学习明确的词汇表。在第一阶段的声学单元发现阶段，我们用 HuBERT 替换对比预测编码特征。在第二阶段的词语分割之后，我们通过平均 HuBERT 特征获得每个段落的声学词嵌入。使用 K-means 对这些嵌入进行聚类，以获得一个词汇表。结果是具有良好覆盖率的分割，其词汇表在 ZeroSpeech 基准测试中达到了最先进的性能。