GPD training of dynamic programming-based speech recognizers.
Takashi Komori, Shigeru Katagiri · Journal of the Acoustical Society of Japan (E) · 1992
Although many pattern classifiers based on artificial neural networks have been vigorous ly studied, they are still inadequate from a viewpoint of classifying dynamic (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.