Flexible feature spaces based on generalized heteroscedastic linear discriminant analysis

This paper presents a generalized feature projection scheme which allows each feature dimension to be classified in a set of 1 to M classes, where M is the total number of classes. Our method is an extension of the classical full-space null-space approach where each dimension can only be classified in either M classes or 1 class. We believe that this more general formulation allows for a better trade-off of number of parameters versus model complexity, which in turn should provide better classification. We first tested GLDA on TIMIT and obtained an improvement up to 1% in phone classification rate over the best HLDA classifier. Preliminary results on Wall Street Journal 20K also show an improvement over the best HLDA system of about 0.2% absolute.