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. 4. The method of claim 1, wherein a scalar quantization code book for the discrete distributions is shared among distributions existing in an identical dimension of all or a part of the probabilistic models.