Contributed articleAnalysis of the correlation structure for a neural predictive model with application to speech recognition☆
References (21)
A generalized hidden Markov model with stateconditioned trend functions of time for the speech signal
Signal Processing
(1992)- et al.
Large vocabulary word recognition using context-dependent allophonic hidden Markov models
Computer Speech and Language
(1990) - et al.
Multilayer feed-forward networks are universal approximators
Neural Networks
(1989) An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes
Inequalities
(1972)- et al.
Time series analysis—forecasting and control
Approximation by superpositions of a Sigmoidal function
Mathematics of Control, Signals, and Systems
(1989)- et al.
Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences
IEEE Transactions on Acoustics, Speech, and Signal Processing
(1980) - et al.
Structural design of a hidden Markov model based speech recognizer using multi-valued phonetic features: Comparison with segmental speech units
Journal of the Acoustical Society of America
(1992) - et al.
Phonemic hidden Markov models with continuous mixture output densities for large vocabulary word recognition
IEEE Transactions on Signal Processing
(1991) - et al.
Modeling acoustic transitions in speech by state-interpolation hidden Markov models
IEEE Transactions on Signal Processing
(1992)
There are more references available in the full text version of this article.
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An earlier shortened version of this paper was presented at the first IEEE Workshop on Neural Networks for Signal Processing, September, 1991, Princeton, NJ.
Copyright © 1994 Published by Elsevier Ltd.