Patent ID: 7209881

Claim:
A method for preparing an acoustic model used for speech recognition using sufficient statistics, each of which is represented by a hidden Markov model, each state of the hidden Markov model being represented by mixed Gaussian distributions, the sufficient statistics including, as information, a mean value, a variance value and an EM count value of the mixed Gaussian distributions and an identification number given to a Gaussian distribution included in the mixed Gaussian distributions, the method preparing an acoustic model represented by a hidden Markov model and used for speech recognition by performing statistics calculation using a mean value, a variance value and an EM count value among Gaussian distributions of a same identification number for two or more different sufficient statistics, to obtain a new Gaussian distribution, and the method comprising the steps of: (a) grouping noise-superimposed speech data according to acoustic similarity; (b) preparing sufficient statistics for each of groups obtained in the step (a) as a material for preparing an acoustic model from a predetermined initial-value sufficient statistic using the speech data in the group; (c) selecting a group acoustically similar to voice data of a user of the speech recognition from the groups obtained in the step (a); (d) selecting a plurality of sufficient statistics acoustically similar to the voice data of the user from the sufficient statistics in the group selected in the step (c); and (e) preparing an acoustic model by performing statistics calculation using a mean value, a variance value and an EM count value among Gaussian distributions of a same identification number for the sufficient statistics selected in the step (d), to obtain a new Gaussian distribution, wherein in the step (a), the noise super-imposed speech data is grouped so that a Gaussian distribution of the predetermined initial-value sufficient statistic and a Gaussian distribution of a sufficient statistic of the noise-superimposed speech data, whose distribution distance is closest to the Gaussian distribution of the predetermined initial-value sufficient statistic will have a same identification number when the sufficient statistic of the noise-super-imposed speech data is prepared from the predetermined initial-value sufficient statistic using the noise-superimposed speech data.