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Text segmentation and topic tracking on broadcast news via a hidden Markov model approach

Expertise in the automatic transcription of broadcast speech has progressed to the point of being able to use the resulting tran-scripts for information retrieval purposes. In this paper, we ﬁrst describe a corpus of automatically recognized broadcast news, a method for segmenting the broadcast into stories, and ﬁnally apply this method to retrieve stories relating to a speciﬁc topic. The method is based on Hidden Markov Models and is in analogy with the usual implementation of HMMs in speech recognition.