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Residual speech signal compression: an experiment in the practical application of neural network technology

Published: 01 June 1990 Publication History

Abstract

Neural networks are a popular area of research today. However, neural network algorithms have only recently proven valuable to application problems. This paper seeks to aid in the process of transferring neural network technology from research to a development environment by describing our experience in applying this technology.
The application studied here is Speaker Identity Verification (SIV), which is the task of verifying a speaker's identity by comparing the speaker's voice pattern to a stored template.
In this paper, we describe the application of the back-propagation neural network algorithm to one aspect of the SIV problem, called Residual Compression (RC). The RC problem is to extract useful features from a part of the speech signal that was not utilized by previous SIV systems. Here, we describe a neural network architecture, pre-processing algorithm, training methodology, and empirical results for this problem. We also present a few guidelines for the use of neural networks in applied settings.

References

[1]
B AUM, E. B., AND HAUSSLER, D. What size net gives Valid Generalization? In Advances in Neural Information Processing Systems 1: {Collected papers of the IEEE Conference on Neural Information Processing Systems - Natural and Synthetic, Denver, Nov. 28-Dec. 1,1988}, D. S. Touretzky, Ed. Morgan Kaufmann, San Mateo, CA, 1989, pp. 81-90.
[2]
DENKER, J., SCHWARTZ, D., WITrNER, B., SOLLA, S., HOPFiELD, J., HOWARD, R., AND JACKEL, L. Automatic Learning, Rule Extraction, and Generalization, October 1987. Unpublished technical report.
[3]
ELMAN, J. L., AND ZIPSER, D. Learning the Hidden Structure of Speech. Tech. Rep. ICS-8701, Institute for Cognitive Science, February 1987.
[4]
FEUSTEL, T. C., AND VELIUS, G. A. Voice-Based Securry: identity Verification over Telephone Lines. In Proceedings of the IEEE Global Telecommunications Conference & Exhibition (Dallas, TX, 1989), vol. 1, IEEE, pp. 212-216.
[5]
HANSON, S. J., AND PRATI', L. Y. Comparing Biases for Minimal Network Construction with Backpropagation. In Advances in Neural Information Processing Systems 1: {Collected papers of the {EEE Conference on Neural Information Processing Systems - Natural and Synthetic, Denver, Nov. 28-Dec. 1,1988}, D. S. Touretzky, Ed. Morgan Kaufmann, San Mateo, CA, 1989, pp. 177-185.
[6]
LAPEDES, A., AND F^RBER, R. Nonlinear signal processing using Neural Networks: Prediction and system modelling. Tech. rep., Theoretical Division, Los Alarnos National Laboratory, Los Alamos, NM 87545, 1987.
[7]
MCCLELLAND, J. L., AND RUMELHART, D. E. Explorations in Parallel Distributed Processing' A Handbook of Models, Programs, and Exercises. The MIT Press, Cambridge, MA, 1988.
[8]
MCCLELLAND, J. L., RUMELHART, D. E., AND THE PDP RESEARCH GROUP. Parallel Distributed Processing, Volume 2: Psychological and Biological models. Addison-Wesley, 1986.
[9]
MORGAN, N., AND BOURLARD, H. Generalization and Parameter Estimation in Feedforward Nets: Some Experiments. In Advances in Neural Information Processing Systems 2, D. S. Touretzky, Ed. Morgan Kaufmann, San Mateo, CA, 1990.
[10]
OPPENHEIM, A. V., AND SCHAFER, R. W. Digital Signal Processing. Prentice-Hall, Englewood Cliffs, New Jersey, 1975.
[11]
RUMELHART, D. E., MECLELLAND, J. L., AND THE PDP RESEARCH GROUP, Eds. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1. MIT Press: Bradford Books, 1987.
[12]
TESAI3RO, G., AND JANSSEN$, R. Scaling relationships in back-propagation learning: dependence on predicate order. Tech. Rep. CCSR-88-1, Center for Complex Systems Research, University of Illinois at Urbana- Champaign, February 1988.
[13]
VELIUS, G. Speaker Identity Feature Combination with a Neural Net. J. Acoust. Soc. Am. Suppl. 1 84 (1988), $61.
[14]
VELIUS, G. A. Variants of Cepstrum Based Speaker Identity Verification. ICASSP 88 J (1988), 583-586.

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cover image ACM Conferences
IEA/AIE '90: Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 2
June 1990
591 pages
ISBN:0897913728
DOI:10.1145/98894
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Published: 01 June 1990

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