Performance of a Backpropagation Neural Network in Diagnostic Rhyme Test Word Recognition

Abstract

This paper investigates isolated speaker- dependent word recognition of Diagnostic Rhyme Test words using a backpropagation neural network classifier. The performance of K-nearest neighbors and closest-class-mean classifiers are compared for several signal- to-noise ratios. The test and training data consisted of 40 frames of weighted eighth- order cepstral coefficients extracted from each word utterance. The neural network classifier correctly classified more than 92% of 2,400 testing examples not contained in the training data for the noise-free case. This performance was better than that of the K-nearest neighbor classifier, which was greater than 83%, and that of the closest class mean classifier, which was greater than 85%.