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. 2. The method of claim 1, wherein the calculating step includes the steps of: obtaining a scalar quantization code by scalar quantizing a value of each dimension component of the input feature vector; determining a probability of each dimension of each mixture component distribution by referring to a scalar quantization code book according to the scalar quantization code; calculating a product of probabilities of all dimensions of each mixture component distribution as a probability of each mixture component distribution; and setting a sum or a maximum value of probabilities of all mixture component distributions within each state as a probability of each probabilistic model in each state with respect to the input feature vector.