Abstract
Although many pattern classifiers based on artificial neural networks have been vigorous-ly studied, they are still inadequate from a viewpoint of classifyingdynamic (variable-and unspecified-duration) speech patterns. To cope with this problem, the generalized probabilistic descent method (GPD) has recently been proposed. GPD not only allows one to train a discriminative system to classify dynamic patterns, but also possesses a remarkable advantage, namely guaranteeing the learning optimality (in the sense of a probabilistic descent search). A practical implementation of this theory, however, remains to be evaluated. In this light, we particularly focus on evaluating GPD in designing a widely-used speech recognizer based on dynamic time warping distance-measurement. We also show that the design algorithm appraised in this paper can be considered a new version of learning vector quantization, which is incorporated with the dynamic programming. Experimental evaluation results in tasks of classifying syllables and phonemes clearly demonstrate GPD's superiority.
