Neural networks for nonlinear discriminant analysis in continuous speech recognition
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چکیده
In this paper neural networks for Nonlinear Discrimi nant Analysis in continuous speech recognition are pre sented Multilayer Perceptrons are used to estimate a posteriori probabilities for Hidden Markov Model states which are the optimal discriminant features for the sepa ration of the HMM states The a posteriori probabilities are transformed by a principal component analysis to calcu late 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 compa red to the standard Linear Discriminant Analysis technique
منابع مشابه
Neural Network Based Nonlinear Discriminant Analysis for Speech Recognition
Neural networks have been one of the most successful recognition models for automatic speech recognition systems because of their high discriminative power and adaptive learning. In many speech recognition tasks, especially for discrete speech classification, it has been shown that neural networks are very powerful for classifying short-time acoustic-phonetic units, such as individual phonemes....
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تاریخ انتشار 1995