Constrained corrective training for continuous parameter system

Details Constrained corrective training for continuous parameter system Constrained corrective training for continuous parameter system

1997-01-03

2000-03-28

Hudspeth, David R.

Data processing: speech signal processing, linguistics, language

Speech signal processing

Recognition

704244, G01L 900

Patent

active

060443448

ABSTRACT:
A method is provided for training a statistical pattern recognition decoder on new data while preserving its accuracy of old, previously learned data. Previously learned data are represented as constrained equations that define a constrained domain (T) in a space of statistical parameters (K) of the decoder. Some part of a previously learned data is represented as a feasible point on the constrained domain. A training procedure is reformulated as optimization of objective functions over the constrained domain. Finally, the constrained optimization functions are solved. This training method ensures that previously learned data is preserved during iterative training steps. While an exemplary speech recognition decoder is discussed, the inventive method is also suited to other pattern recognition problems such as, for example, handwriting recognition, image recognition, machine translation, or natural language processing.

REFERENCES:
Baum et al "A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains" 1970, The annals of mathematics statistics, vol. 41, No. 1, pp. 164-171.
Kanevsky "A generalization of the Baum algorithim to functions on non-linear manifolds" May 1995, 1995 conference on acoustics, speech, and signal processing, vol. 1, pp. 473-476.
Salgado et al "Optimal power flow solutions using the gradient projection method" Nov. 1990, IEE proceedings, pp. 424-428.
Gopalakrishnan et al. An Inequality for Rational Functions with Applications to Some Statistical Estimation Problems, IEEE TRabsactions on Informatioon Theory, vol. 37 No. 1, Jan. 1991.
Deller "Discrete-time processing of speech signals" Prentice-Hall pp. 692,705-714,738, 1987.
Bahl et al "A maximum likelihood approach to continuous speech recognition" IEEE pp. 179-190, Mar. 1983.

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