1. A method of setting an optimum-partitioned classified neural network, comprising: obtaining a number of L phoneme combinations having names of left and right phonemes by using a phoneme boundary obtained by manual labeling; generating a number of K neural network combinations of an MLP type from learning data including input variables; searching for neural networks having minimum errors with respect to the L phoneme combinations from the neural network combinations, and classifying the L phoneme combinations into K phoneme combination groups searched with the same neural networks; using the K phoneme combination groups classified in the searching for neural networks, updating weights until individual errors of the neural networks have converged during learning with applicable learning data for the K neural networks; and repeatedly performing the searching for neural networks and the updating weight, corresponding to the K neural networks of which the individual errors have converged, until a total error sum of K neural networks, of which individual errors have converged in the updating weights, has converged, and composing an optimum-partitioned classified neural network combination using the K neural networks obtained when the total error sum has converged.