1. A pattern recognition apparatus, comprising: an input unit for inputting input vectors; a parameter extraction unit for extracting a parameter expressing a condition of pattern recognition and probabilistic model training from each input vector; an initial condition probabilistic model creation and storage unit for creating and storing an initial condition probabilistic model from the parameter expressing the condition extracted from the input vector inputted under an initial condition at a time of acquiring training data; a reference probabilistic model storage unit for storing prescribed reference probabilistic models corresponding to a prescribed value of the parameter expressing the condition; an initial condition imposed probabilistic model creation and storage unit for creating and storing initial condition imposed probabilistic models from the initial condition probabilistic model and the reference probabilistic models; a Jacobian matrix calculation and storage unit for calculating and storing Jacobian matrices of a Taylor expansion expressing a change in a model parameter in terms of a change in the parameter expressing the condition, from the initial condition probabilistic model and the initial condition imposed probabilistic models; a difference calculation unit for calculating a difference between the initial condition probabilistic model and an adaptation target condition probabilistic model obtained from the parameter expressing the condition which is extracted from the input vector inputted under a current condition at a time of actual recognition; an adapted condition imposed probabilistic model calculation and storage unit for calculating and storing adapted condition imposed probabilistic models from the difference, the initial condition imposed probabilistic models, and the Jacobian matrices; and a pattern recognition unit for carrying out a pattern recognition by calculating a likelihood of each adapted condition imposed probabilistic model expressing features of each recognition category with respect to the input vector and outputting a recognition category expressed by an adapted condition imposed probabilistic model with a highest likelihood among the adapted condition imposed probabilistic models as a recognition result.