11. A pattern recognition apparatus, comprising: a calculation unit for 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 a recognition unit for outputting a recognition category expressed by a probabilistic model with a highest probability among a plurality of probabilistic models as a recognition result.
16. The apparatus of claim 11, wherein the calculation unit uses the discrete distributions are distributions based on quantization points defined by a non-linear scalar quantization, and the quantization points are given in a tree structure.