Performance Of A Backpropagation Neural Network In Diagnostic Rhyme Test Word Recognition  

Oleh:

Tsang, Chit-Sang ; Villamar, Carlos R.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Simulation vol. 70 no. 3 (Mar. 1994), page 167-174.
Topik: Neural networks; backpropagation; speech recognition; diagnostic rhyme test; DRT
Fulltext: 167.pdf (596.46KB)

Isi artikel

This paper investigates isolated speakerdependent 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 signalto-noise ratios. The test and training data consisted of 40 frames of weighted eighthorder 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%.

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