Optimal linear feature transformations for semi-continuous hidden Markov models

Linear discriminant or Karhunen-Loeve transforms are established techniques for mapping features into a lower dimensional subspace. This paper introduces a uniform statistical framework, where the computation of the optimal feature reduction is formalized as a maximum-likelihood estimation problem. The experimental evaluation of this suggested extension of linear selection methods shows a slight improvement of the recognition accuracy.