TY  - RPRT
T1  - Neural networks for nonlinear discriminant analysis in continuous speech recognition
T3  - Saarbrücken, 1996
A1  - Reichl,W.
A1  - Harengel,S.
A1  - Wolfertstetter,F.
A1  - Ruske,G.
Y1  - 2011/09/06
N2  - 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.
KW  - Künstliche Intelligenz
CY  - Saarbrücken
PB  - Universitäts- und Landesbibliothek
AD  - Postfach 151141, 66041 Saarbrücken
UR  - http://scidok.sulb.uni-saarland.de/volltexte/2011/4193
ER  -