nips nips2001 nips2001-99 nips2001-99-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Jeff Bilmes, Gang Ji, Marina Meila
Abstract: In this work, we introduce an information-theoretic based correction term to the likelihood ratio classification method for multiple classes. Under certain conditions, the term is sufficient for optimally correcting the difference between the true and estimated likelihood ratio, and we analyze this in the Gaussian case. We find that the new correction term significantly improves the classification results when tested on medium vocabulary speech recognition tasks. Moreover, the addition of this term makes the class comparisons analogous to an intransitive game and we therefore use several tournament-like strategies to deal with this issue. We find that further small improvements are obtained by using an appropriate tournament. Lastly, we find that intransitivity appears to be a good measure of classification confidence.
[1] J. Bilmes. Natural Statistic Models for Automatic Speech Recognition. PhD thesis, U.C. Berkeley, Dept. of EECS, CS Division, 1999.
[2] T.M. Cover and J.A. Thomas. Elements of Information Theory. Wiley, 1991.
[3] R.O. Duda, P.E. Hart, and D.G. Stork. Pattern Classification. John Wiley and Sons, Inc., 2000.
[4] J. Pitrelli, C. Fong, S.H. Wong, J.R. Spitz, and H.C. Lueng. PhoneBook: A phonetically-rich isolated-word telephone-speech database. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, 1995.
[5] P.D. Straffin. Game Theory and Strategy. The Mathematical ASsociation of America, 1993.