Design method for a pattern classifier suited to adaptation

This paper describes a method for designing a pattern classifier that will perform well after it has been adapted to changes in input conditions. Considering the off-line (batch-mode) adaptation methods which are based on the transformation of classifier parameters, we formulate the problem of designing classifiers, and propose a method for training them. In the proposed training method, the classifier is trained while the adaptation is being carried out. The objective function for the training is given based on the recognition performance obtained by the adapted classifier. The utility of the proposed training method is demonstrated by experiments in a five-class Japanese vowel pattern recognition task with speaker adaptation.