Latent Dirichlet allocation (LDA) is widely used for unsupervised topic
modelling on sets of documents. No temporal information is used in the model.
However, there is often a relationship between the corresponding topics of
consecutive tokens. In this paper, we present an extension to LDA that uses a
Markov chain to model temporal information. We use this new model for acoustic
unit discovery from speech. As input tokens, the model takes a discretised
encoding of speech from a vector quantised (VQ) neural network with 512 codes.
The goal is then to map these 512 VQ codes to 50 phone-like units (topics) in
order to more closely resemble true phones. In contrast to the base LDA, which
only considers how VQ codes co-occur within utterances (documents), the Markov
chain LDA additionally captures how consecutive codes follow one another. This
extension leads to an increase in cluster quality and phone segmentation
results compared to the base LDA. Compared to a recent vector quantised neural
network approach that also learns 50 units, the extended LDA model performs
better in phone segmentation but worse in mutual information.

本文介绍了一种扩展 LDA 模型的方法，利用马尔可夫链来建模时间信息，从而用于语音信号的无监督单元发现。与基本的 LDA 模型相比，扩展后的模型可以更好地描述相邻单元之间的关系，从而得到更好的语音单元划分结果。