Abstract

This study explores the possibility of using Condensed Nearest Neighbor (CNN) rule for classification in various word recognition problems. A modified version of “Condensing” combined with “Editing” algorithm is implemented to select the reference templates for a speaker independent isolated word recognition problem. It is shown that these algorithms improve the recognition rate in comparison to using clustering techniques for template selection.

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