4. A speech recognition apparatus comprising: a first storing unit configured to store a first acoustic model invariable regardless of speaker and environment; a second storing unit configured to store second acoustic models that vary in accordance with at least either one of a specific speaker and a specific environment; a third storing unit configured to store a classification model to classify the second acoustic models into different groups, the classification model being a hidden Markov model that has, as an output probability distribution, a mixed normal distribution in which a plurality of normal distributions are weighted in accordance with weighting factors and combined together, having a structure, a state transition probability, and a mean vector and variance-covariance matrix of all output probability distributions as shared parameters with the first acoustic model, and weighting factors of distributions as non-shared parameters with the first acoustic model; a first recognizing unit configured to recognize input speech by applying the first acoustic model and the non-shared parameters of the classification model to the input speech to obtain a plurality of candidate words having relatively high first likelihoods with respect to the input speech and second likelihood for the groups of the input speech; a selecting unit configured to select a group that has a largest value for the second likelihood from among the groups; and a second recognizing unit configured to recognize the input speech by applying a second acoustic model that belongs to the selected group onto the input speech to calculate a third likelihood for each of the candidate words and obtain as the recognition result a candidate word that has a largest value for the third likelihood.
4. A speech recognition apparatus comprising: a first storing unit configured to store a first acoustic model invariable regardless of speaker and environment; a second storing unit configured to store second acoustic models that vary in accordance with at least either one of a specific speaker and a specific environment; a third storing unit configured to store a classification model to classify the second acoustic models into different groups, the classification model being a hidden Markov model that has, as an output probability distribution, a mixed normal distribution in which a plurality of normal distributions are weighted in accordance with weighting factors and combined together, having a structure, a state transition probability, and a mean vector and variance-covariance matrix of all output probability distributions as shared parameters with the first acoustic model, and weighting factors of distributions as non-shared parameters with the first acoustic model; a first recognizing unit configured to recognize input speech by applying the first acoustic model and the non-shared parameters of the classification model to the input speech to obtain a plurality of candidate words having relatively high first likelihoods with respect to the input speech and second likelihood for the groups of the input speech; a selecting unit configured to select a group that has a largest value for the second likelihood from among the groups; and a second recognizing unit configured to recognize the input speech by applying a second acoustic model that belongs to the selected group onto the input speech to calculate a third likelihood for each of the candidate words and obtain as the recognition result a candidate word that has a largest value for the third likelihood. 5. The apparatus according to claim 4 , wherein the first speech recognizing unit calculates the output of normal distributions by use of features of the input speech and the shared parameters, multiplies the output of normal distributions by the weighting factors of the first acoustic model to combine, and then multiplies products by state transition probabilities to obtain the first likelihoods, and in parallel, the first speech recognizing unit multiplies weighting factors corresponding to the groups by the normal distributions to combine and then multiplies products by the state transition probabilities to obtain the second likelihood.