Maximum entropy classification applied to speech

M. R. Gupta, M. P. Friedlander, and R. M. Gray. In Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers, vol. 2, 1480-1483, October 2000 PDF

Abstract

We present a new method for classification using the maximum entropy principle, allowing full use of relevant training data and smoothing the data space. To classify a test point we compute a maximum entropy weight distribution over a subset of training data and constrain the weights to exactly reconstruct the test point. The classification problem is formulated as a linearly constrained optimization problem and solved using a primal-dual logarithmic barrier method, well suited for high-dimensional data. We discuss theoretical advantages and present experimental results on vowel data which demonstrate that the method performs competitively for speech classification tasks.

BibTeX

 @inproceedings{GuptFrieGray:2000,
  Address = {Pacific Grove, California},
  Author = {M. R. Gupta and M. P. Friedlander and R. M. Gray},
  Booktitle = {Conference Record of the Thirty-Fourth Asilomar
               Conference on Signals, Systems and Computers, 2000.},
  Month = {October},
  Pages = {1480-1483},
  Title = {Maximum entropy classification applied to speech},
  Volume = {2},
  Year = 2000
 }