Method and system for continuous speech recognition using voting

Details Method and system for continuous speech recognition using voting Method and system for continuous speech recognition using voting

1994-10-26

1997-06-10

Knepper, David D.

395 241, 395 263, G10L 500

Patent

active

056384866

ABSTRACT:
In a speech-recognition system having a plurality of classifiers, a voting window includes a sequence of outputs from each of the classifiers. For each classifier, a voting sum is generated corresponding to the voting window. A spoken sound is identified by determining which classifier corresponds to the greatest voting sum.

REFERENCES:
patent: 3349183 (1967-10-01), Campanella
patent: 3697703 (1972-10-01), Clark, Jr. et al.
patent: 3946157 (1976-03-01), Dreyfus
patent: 4405838 (1983-09-01), Nitta et al.
patent: 4624008 (1986-11-01), Vensko et al.
patent: 4718093 (1988-01-01), Brown
patent: 4761815 (1988-08-01), Hitchcock
patent: 4852172 (1989-07-01), Taguchi
patent: 4852180 (1989-07-01), Levinson
patent: 4945566 (1990-07-01), Mergel et al.
patent: 5042001 (1991-08-01), Brightman et al.
patent: 5072452 (1991-12-01), Brown et al.
patent: 5073867 (1991-12-01), Murphy et al.
patent: 5239594 (1993-08-01), Yoda
patent: 5305422 (1994-04-01), Junqua
patent: 5329608 (1994-07-01), Bocchieri et al.
patent: 5365592 (1994-11-01), Horner et al.
patent: 5369726 (1994-11-01), Kroeker et al.
patent: 5404422 (1995-04-01), Sakamoto et al.
patent: 5450522 (1995-09-01), Hermansky et al.
Jerry M. Rosenberg, Dictionary of Computers, Information Processing, and Telecommunications, 2nd Edition, p. 88.
Fundamentals of Speech Recognition, Lawrence Rabiner et al., Published by Prentice Hall, Inc., 1993, pp. 122-132.
Speech Recognition by Computer, Stephen E. Levinson et al., Scientific American, Apr. 1981, pp. 64-76.
On the Use of Bandpass Liftering in Speech Recognition, Biing-Hwang Juang et al., IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-35, No. 7, Jul. 1987.
Highly-automated, Non-parametric statistical learning for autonomous target recognition, Keith C. Drake, AbTech Corporation, 700 Harris Street, Charlottesville, Virginia 22903, pp. 1-10, Proceedings of the SPI 20th Applied Imagery Pattern Recognition Workshop, Oct. 1991, McLean, Virginia.
On the Design Principles of the Functional Link Nets, Jun Wang and Edilberto P. Teixeira, Department of Systems Engineering, Case Western Reserve University, Cleveland, Ohio 44106, pp. 613-616, Ieee International Conf. on Systems Engineering, Aug. 9-11, 1990, Vista International Hotel, Pittsburgh, Pennsylvania, IEEE Catalog No:90CH2872-0.
A Neural Feed-Forward Network with a Polynomial Nonlinearity, Nils Hoffmann, Electronics Institute, Building 349, Technical University of Denmark, DK-2800 Lyngby, Denmark, Neural Networks for Signal Processing, Proceedings of the IEEE-SP Workshop, Aug. 31-Sep. 2, 199?, pp. 49-58.
A Polynomial time Algorithm for Generating Neural Networkes for Classification Problems, Asim Roy and Somnath Mukhopadhyay, Dept. of Decision and Information Systems, Arizona State University, IJCNN, 1992, 0-7803-0559-0/92 IEEE, pp. I-174-I-152.
Modeling systems with Polynomial Networks, Tools for Predicting Behavior, Peter D. Varhol, Programmer's Workbench, Dr. Dobb's Journal, Sep. 1993, Begins on p. 76.
Polynomial Functions Can Be Realized by Finite Size Multilayer Feedforward Neural Networks, Naohiro TODA, Ken-ichi FUNAHASHI and Shiro USUI, Department of Information and Computer Sciences, Toyohashi University of Technology, Tempaku, Toyohashi 441, Japan, 1991 IEEE International Joint Conference on Neural Networks, vol. 1of3, The Westin Stamford and Westin Plaza, 18-21, Nov. 1991, Singapore.
An Artificial Neural Networks for Approximating Polynomial Functions, Behnam Malakooti and YingQing Zhou, Department of Ssytems Engineering, Center for Automation and Intelligent Systmes Research, Case Western Reserve University, Cleveland, Ohio, International Joint Conference on Neural Networks, Jun. 1992, pp. III-966-III-971.
Robot Kinematics Learning Computations Using Polynomial Neural Networks, C.L. Philip Chen and A.D. McAulay, Department of Computer Science and Engineering, Wright State University, Dayton, OH 45435, Proceedings of the 1991 IEEE, International Cof. on Robotics and Automation, Sacramento, CA, Apr. 1991, pp. 2638-2643.
Poloynomial and Standard Higher Order Neural Network, Chir-Ho Cbang et al., 1993 International Conference on Neural Networks, Mar. 28-Apr. 1, 1993, pp. 989-994.
Speech Coding and Recognition: A Review, Andreas S. Spanias et al., IEICE Trans. Fundamentals, vol. E75-A, No. 2, Feb. 1992.
A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Lawrence R. Rabiner, Proceedings of the IEEE, vol. 77, No. 2, Feb. 1992.

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