8. The modeling generating unit according to claim 7 , wherein a probability density function used by the temporary model merging unit for merging of the temporary language model is expressed by 
 p ′( x|s )=λ 1 p ( x|s )+(1−λ 1 ) p ( x|d 1 ) p ( d 1 |s ) where x is an observation feature vector of voice to be recognized, s is a hidden Markov state in the standard model, d 1 is a hidden Markov state in the first model, and λ 1 is a linear interpolating coefficient such that 0<λ 1 <1, and wherein a probability density function used by the speech recognition model merging unit for merging of the speech recognition model is expressed by p ′′ ⁡ ( x | s ) = ∑ k = 1 K ⁢ w k ( sc ) ′ ⁢ N k ( sc ) ⁡ ( · ) + ∑ m = 1 M ⁢ ∑ n = 1 N ⁢ w mn ( dc ⁢ ⁢ 1 ) ′ ⁢ N mn ( dc ⁢ ⁢ 1 ) ⁡ ( · ) + ∑ p = 1 P ⁢ ∑ q = 1 Q ⁢ w pq ( dc ⁢ 2 ) ′ ⁢ N pq ( dc ⁢ ⁢ 2 ) ⁡ ( · ) where w k (sc)′ is a mixture weight for the hidden Markov state of the standard model, w mn (dc1)′ is mixture weight for the hidden Markov state of the first model, w pq (dc2)′ is a mixture weight for the hidden Markov state of the second model, K is the number of Gaussian mixtures for Hidden Markov Model state s in the standard model, N k (sc) (•) is an element of Gaussian mixture for Hidden Markov Model state s, M is the number of d 1 that is considered as the pronunciation variants occurring between the first model for d 1 and the standard model, N is the number of Gaussian mixtures for Hidden Markov Model state d 1 in the first model, N mn (dc1) (•) is an element of Gaussian mixture for Hidden Markov Model state d 1 , P is the number of d 2 that is considered as the pronunciation variants occurring between the second model for d 2 and the standard model, Q is the number of Gaussian mixtures for Hidden Markov Model state d 2 in the second model, N pq (dc2) (•) is an element of Gaussian mixture for Hidden Markov Model state d 2 .
7. A model generating unit, controlled by a control unit, system for creating a speech recognition model, the model generating unit comprising: a standard common-language training database, which stores training data of a triphone standard common language; a first development database, which stores first development data of a monophone dialectal-accented common language based on a first accent of the standard common language; a second development database, which stores second development data of a monophone dialectal-accented common language based on a second accent of the standard common language that is different from the first accent; a standard model generator, which generates a standard model based on training data stored in the standard common-language training database; a first model generator, which generates a first model based on development data of a first accent of the standard common language stored in the first development database; a temporary model merging unit, which creates a temporary language model by merging, through an iterative process, the standard model and the first model with reference to a first confusion matrix formed, by a first confusion matrix generator, between the standard model and the development data of the first accent stored in the first development database; a second model generator, which generates a second model based on development data of a second accent of the standard common language stored in the second development database; and a speech recognition model merging unit, which creates the speech recognition model by merging, through an iterative process, the second model and the temporary language model with reference to a second confusion matrix formed, by a second confusion matrix generator, between the temporary model and the development data of the second accent stored in the second development database.