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%.

Keywords

Neural networks backpropagation speech recognition diagnostic rhyme test DRT

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References

Voiers, W.D. 1979. "Intelligibility Testing at Dynastat: The Diagnostic Rhyme Test." Dynastat Report, December 1979, pp. 1-10.

Voiers, W.D. 1983. "Evaluating Processed Speech Using the Diagnostic Rhyme Test." Speech Technology Magazine , Jan/ Feb 1983, pp. 30-39.

Rabiner, L.R. and Sambur, M.R. 1975. "An Algorithm for Determining the Endpoints of Isolated Utterances," Bell Systems Technical Journal, vol. 54, pp. 297-315.

Dell, R.D. , Proakis, J.G. and Hansen, J.H.L. 1993. Discrete-Time Processing of Speech Signals, Macmillan Publishing Company.

Rabiner, L.R. 1989. "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition." Proceedings of IEEE, vol. 77, no. 2, pp. 276-278.

Cottrell, G.W. , Munro, P.W. and Zipser, D. 1987. "Learning Internal Representations from Gray Scale Images: An Example of Extensional Programming." Proceedings of the Ninth Annual Conference of the Cognitive Science Society, pp. 461-473.

Gorman, R.P. and Sejnowski, T.J. 1988. "Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets." Neural Networks, vol. 1, pp. 75-89.

Itakura, F. 1975. "Minimum Prediction Residual Principle Applied to Speech Recognition." IEEE Transactions on ASSP, vol. ASSP-23, no. 1, pp. 67-72.

Elman, J.L. and Zipser, D. 1987. "Learning the Hidden Structure of Speech." ICS Report 8701, Feb. 1987, pp. 1-17.

10.

Mirchandini, G. and Cao, W. 1989. "On Hidden Nodes in Neural Nets." IEEE Transactions on Circuits and Systems, vol. 36, pp. 661-664.