Consistent subsets in speech recognition systems

In the paper the method of the transformation of the learning samples into their representatives is presented. The proposed algorithm combines the features of the neural nets approach, i.e. the representatives lie near the boundaries separating the classes, and cluster seeking approach — each representative corresponds to the group of elements lying close to each other. By using the consistent subset the drawbacks of those approaches (cluster can comprise samples from different classes; the sophisticated network is not appropriate in the regions where the classes overlap) can be avoided in some cases. Several applications in the area of speech recognition are presented.