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MCE Training Techniques for Topic Identification of Spoken Audio Documents

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1 Author(s)
Timothy J. Hazen ; MIT Lincoln Laboratory, Lexington, MA, USA

In this paper, we discuss the use of minimum classification error (MCE) training as a means for improving traditional approaches to topic identification such as naive Bayes classifiers and support vector machines. A key element of our new MCE training techniques is their ability to efficiently apply jackknifing or leave-one-out training to yield improved models which generalize better to unseen data. Experiments were conducted using recorded human-human telephone conversations from the Fisher Corpus using feature vector representations from word-based automatic speech recognition lattices. Sizeable improvements in topic identification accuracy using the new MCE training techniques were observed.

Published in:

IEEE Transactions on Audio, Speech, and Language Processing  (Volume:19 ,  Issue: 8 )