5th European Conference on Speech Communication and Technology

Rhodes, Greece
September 22-25, 1997

Nonlinear Discriminant Analysis for Improved Speech Recognition

Vincent Fontaine, Christophe Ris, Jean-Marc Boite

Faculte Polytechnique de Mons, Mons, Belgium

Linear Discriminant Analysis (LDA) has been widely applied to speech recognition resulting in improved recognition performance and improved robustness. LDA designs a linear transformation that projects a m-dimensional space on a m-dimensional space (m < n) such that the class separability is maximum. This paper presents new results related to our previous work [6] on nonlinear discriminant analysis (NLDA) based on the discriminant properties of Artificial Neural Networks (ANN) and more particularly MLP. Experiments performed on the isolated word large vocabulary Phone- book database show that NLDA provides a method for designing discriminant features particularly efficient as well for continuous densities HMM as for hybrid HMM/ANN recognizers.

Full Paper

Bibliographic reference.  Fontaine, Vincent / Ris, Christophe / Boite, Jean-Marc (1997): "Nonlinear discriminant analysis for improved speech recognition", In EUROSPEECH-1997, 2071-2074.