A category-dependent feature selection method for speech signals

We present a novel method of dimension reduction and feature selection that makes use of category-dependent regions in high-dimensional data. Our method is inspired by phonemedependent, noise-robust low-variance regions observed in the cortical response, and introduces the notion of categorydependence in a two-step dimension reduction process that draws on the fundamental principles of Fisher Linear Discriminant Analysis. As a method of applying these features in an actual pattern classification task, we construct a system of multiple speech recognizers that are combined by a Bayesian decision rule under some simplifying assumptions. The results show a significant increase in recognition rate for low signal-to-noise ratios compared with previous methods, providing motivation for further study on hierarchical, category-dependent recognition and detection.