Unsupervised word segmentation in audio utterances is challenging as, in
speech, there is typically no gap between words. In a preliminary experiment,
we show that recent deep self-supervised features are very effective for word
segmentation but require supervision for training the classification head. To
extend their effectiveness to unsupervised word segmentation, we propose a
pseudo-labeling strategy. Our approach relies on the observation that the
temporal gradient magnitude of the embeddings (i.e. the distance between the
embeddings of subsequent frames) is typically minimal far from the boundaries
and higher nearer the boundaries. We use a thresholding function on the
temporal gradient magnitude to define a psuedo-label for wordness. We train a
linear classifier, mapping the embedding of a single frame to the pseudo-label.
Finally, we use the classifier score to predict whether a frame is a word or a
boundary. In an empirical investigation, our method, despite its simplicity and
fast run time, is shown to significantly outperform all previous methods on two
datasets.

提出了一种基于深度自监督特征的无监督词语分割的伪标签策略，通过利用特征嵌入的时间渐变幅度来定义单帧词性的伪标签，并用线性分类器将嵌入映射为伪标签，进而预测出每一帧的词性，试验结果表明该方法在两个数据集上明显优于以前的所有方法。