To generate the voice model, on which discriminating and collating capabilities are maintained at a high level against the speaker's voice that varies depending on the uttering time, and to realize such a speaker recognition that a high recognition performance is maintained while utilizing the model.

The feature parameter string, which is extracted from latest inputted voices in a feature parameter extracting section 1, and the feature parameter string, which is previously stored in a feature parameter storage section 2, are inputted to a model generating section 3. In the section 3, a parameter θ(t) of a hidden Markov model(HMM) is defined as the one obtd. by such a manner that a parameter θ' of the HMM, that is estimated from time independent voice data components, is transformed by a model transformation function G(t), that represents the variation being dependent on time t. Then, the parameter θ' and the function G(t) of each time are estimated based on the inputted feature parameter string at each time. Then, the parameter θ' is stored in a model storage section 4 and used for a speaker recognition when recognition voice data are inputted.