In this paper neural networks for Nonlinear Discriminant Analysis in continuous speech recognition are presented. Multilayer Perceptrons are used to estimate a-posteriori probabilities for Hidden-Markov Model states, which are the optimal discriminant features for the separation of the HMM states. The a-posteriori probabilities are transformed by a principal component analysis to calculate the new features for semicontinuous HMMs, which are trained by the known Maximum-Likelihood training. The nonlinear discriminant transformation is used in speaker-independent phoneme recognition experiments and compared to the standard Linear Discriminant Analysis technique.
Bibliographic reference. Reichl, Wolfgang / Harengel, S. / Wolfertstetter, F. / Ruske, GŁnther (1995): "Neural networks for nonlinear discriminant analysis in continuous speech recognition", In EUROSPEECH-1995, 2163-2166.