1. A pattern recognition method, comprising the steps of: calculating a probability of each probabilistic model expressing features of each recognition category with respect to each input feature vector derived from each input signal, wherein the probabilistic model represents a feature parameter subspace in which feature vectors of each recognition category exist and the feature parameter subspace is expressed by using mixture distributions of one-dimensional discrete distributions with arbitrary distribution shapes which are arranged in respective dimensions; and outputting a recognition category expressed by a probabilistic model with a highest probability among a plurality of probabilistic models as a recognition result.
1. A pattern recognition method, comprising the steps of: calculating a probability of each probabilistic model expressing features of each recognition category with respect to each input feature vector derived from each input signal, wherein the probabilistic model represents a feature parameter subspace in which feature vectors of each recognition category exist and the feature parameter subspace is expressed by using mixture distributions of one-dimensional discrete distributions with arbitrary distribution shapes which are arranged in respective dimensions; and outputting a recognition category expressed by a probabilistic model with a highest probability among a plurality of probabilistic models as a recognition result. 8. The method of claim 1, further comprising the step of: estimating parameters of the discrete distributions from training data, by first training a continuous distribution type model with a number of mixture component distributions greater than a desired number of distributions to be mixed in the mixture distribution, then merging continuous distributions of the continuous distribution type model until a number of mixture component distributions becomes equal to the desired number of distributions, and then training a probabilistic model obtained by discretizing each continuous distribution of the continuous distribution type model after merging as an initial model.