An HMM-based text segmentation method using variational Bayes approach and its application to LVCSR for broadcast news

Recent progress in large vocabulary continuous speech recognition (LVCSR) has raised the possibility of applying information retrieval techniques to the resulting text. This paper presents a novel unsupervised text segmentation method. Assuming a generative model of a text stream as a left-to-right hidden Markov model (HMM), text segmentation can be formulated as model parameter estimation and model selection using the text stream. The formulation is derived based on the variational Bayes framework, which is expected to work well with highly sparse data such as text. The effectiveness of the proposed method is demonstrated through a series of experiments, where broadcast news programs are automatically transcribed and segmented into separate news stories.