16 December 1992 Feature enhancement for multilayer perceptron and semicontinuous hidden Markov model-based classifiers using neural networks
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Abstract
Neural and stochastic models for signal classification generate output probabilities to indicate whether or not their inputs are members of the modeled class. This paper presents a feature enhancing neural network with weights based on the modeled class which can improve the classification performance of single output classifiers, by increasing output probabilities for members of the modeled class or decreasing output probabilities for non-members. The neural network is demonstrated as a front-end for multi-layer perceptron and semi-continuous hidden Markov model based classifiers for speech recognition applications. It is unique in that the weights and width of the input layer adapt based on extracted characteristics from the input speech signal. The connectionist architecture is motivated by the highly successful time-delay neural network and the desire to find efficient training procedures for class-dependent, short- time transformations. The weights are determined using a principal component analysis process and can be found by applying iterative or conventional algorithms. The neural network reduces false acceptances by more than one-third for a defined mono-syllable keyword spotting application using a semi-continuous hidden Markov model based system. An evaluation of the neural network as a front-end for multi-layer perceptron based classifiers which distinguish a word from confusable words is also presented.
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Gregory J. Clary, Gregory J. Clary, John H. L. Hansen, John H. L. Hansen, } "Feature enhancement for multilayer perceptron and semicontinuous hidden Markov model-based classifiers using neural networks", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130858; https://doi.org/10.1117/12.130858
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