Maximum entropy classification applied to speech

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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
}