Nonlinear discriminant analysis with neural networks for speech recognition

Linear Discriminant Analysis (LDA) has been applied successfully to speech recognition tasks, improving accuracy and robustness against some types of noise. However, it is well known that LDA suffers from some weaknesses if the distributions are not unimodal or when the mean of the distributions are shared. In this paper, we propose to take advantage of the nonlinear discriminant properties of the Artificial Neural Networks (ANN) in the task of reducing the dimensionality of the input space, leading to a nonlinear discriminant analysis.