Data modeling of class independent recognition models

Details Data modeling of class independent recognition models Data modeling of class independent recognition models

2011-08-23

2011-08-23

Lerner, Martin (Department: 2626)

Data processing: speech signal processing, linguistics, language

Speech signal processing

Recognition

C704S234000, C704S250000

Reexamination Certificate

active

08005674

ABSTRACT:
A recognition model set is generated. A technique is described to take advantage of the logarithm likelihood of real data for cross entropy to measure the mismatch between a training data and a training data derived model, and compare such type of mismatches between class dependent models and class independent model for evidence of model replacement. By using change of cross entropies in the decision of adding class independent Gaussian Mixture Models (GMMs), the good performance of class dependent models is largely retained, while decreasing the size and complexity of the model.

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“Cross entropy”, definition from Wikipedia, downloaded Apr. 1, 2011, 2 Pages.
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