By Topic

MCE Training Techniques for Topic Identification of Spoken Audio Documents

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Hazen, T.J. ; MIT Lincoln Lab., 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:

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