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。

无监督的词语分割利用时间梯度伪标签

Unsupervised Word Segmentation Using Temporal Gradient Pseudo-Labels

We present an unsupervised word segmentation model, in which the learning
objective is to maximize the generation probability of a sentence given its all
possible segmentation. Such generation probability can be factorized into the
likelihood of each possible segment given the context in a recursive way. In
order to better capture the long- and short-term dependencies, we propose to
use bi-directional neural language models to better capture the features of
segment's context. Two decoding algorithms are also described to combine the
context features from both directions to generate the final segmentation, which
helps to reconcile word boundary ambiguities. Experimental results showed that
our context-sensitive unsupervised segmentation model achieved state-of-the-art
at different evaluation settings on various data sets for Chinese, and the
comparable result for Thai.

本文介绍一种基于上下文敏感的无监督词分割模型，使用双向神经语言模型和两种解码算法来增强长期和短期的相关性，该模型在不同的数据集上实现了最新技术水平的中文和泰文词分割结果。