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Soundbite detection in broadcast news domain

In this paper, we present results of a study designed to identify SOUNDBITES in Broadcast News. We describe a Conditional Ran- dom Field-based model for the detection of these included speech segments uttered by individuals who are interviewed or who are the subject of a news story. Our goal is to identify direct quota-tions in spoken corpora which can be directly attributable to particular individuals, as well as to associate these soundbites with their speakers. We frame soundbite detection as a binary classiﬁcation problem in which each turn is categorized either as a soundbite or not. We use lexical, acoustic/prosodic and structural features on a turn level to train a CRF. We performed a 10-fold cross validation experiment in which we obtained an accuracy of 67.4% and an F-measure of 0.566 which is 20.9% and 38.6% higher than a chance baseline.