Interspeech'2005 - Eurospeech

Lisbon, Portugal
September 4-8, 2005

A Category-Dependent Feature Selection Method for Speech Signals

Woojay Jeon, Biing-Hwang Juang

Georgia Institute of Technology, Atlanta, GA, USA

We present a novel method of dimension reduction and feature selection that makes use of category-dependent regions in highdimensional data. Our method is inspired by phoneme-dependent, noise-robust low-variance regions observed in the cortical response, and introduces the notion of category-dependence 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.

Full Paper

Bibliographic reference.  Jeon, Woojay / Juang, Biing-Hwang (2005): "A category-dependent feature selection method for speech signals", In INTERSPEECH-2005, 365-368.