Utterance-level latent topic transition modeling for spoken documents and its application in automatic summarization

In this paper, we propose to use an utterance-level latent topic transition model to estimate the latent topics behind the utterances, and test the performance of such model in extractive speech summarization. In this model, the latent topic weights behind an utterance are estimated, and these topic weights evolve from an utterance to the next in a spoken document based on a topic transition function represented by a matrix. We explore different ways of obtaining such topic transition matrices used in the model, and find using a set of matrices estimated with utterances clustered from a training spoken document set is very useful. This model was shown to be able to offer extra performance improvement when used with the popularly used Probability Latent Semantic Analysis (PLSA) in preliminary experiments on speech summarization.