1. A standard model creating apparatus for creating a standard model which shows an acoustic characteristic having a specific attribute and is used for speech recognition in an electronic apparatus used by a user, the standard model creating apparatus using a probability model that expresses a frequency parameter showing an acoustic characteristic as an output probability, the standard model creating apparatus comprising: a reference model storing unit configured to store a plurality of reference models which are probability models showing an acoustic characteristic having a specific attribute; and a standard model creating unit configured to create the standard model by calculating statistics of the standard model using statistics of the plurality of reference models stored in said reference model storing unit, wherein said standard model creating unit includes: a standard model structure determining unit configured to determine a structure of the standard model which is to be created, based on specification information regarding specifications of the electronic apparatus; an initial standard model creating unit configured to determine initial values of the statistics specifying the standard model whose structure has been determined; and a statistics estimating unit configured to estimate and calculate the statistics of the standard model so as to maximize or locally maximize a probability or a likelihood of the standard model, whose initial values have been determined, with respect to the plurality of reference models, wherein the plurality of reference models and the standard model are expressed using at least one Gaussian distribution, said standard model structure determining unit is configured to determine a number of statistics of the standard model including at least a number of Gaussian mixture distributions as the structure of the standard model, said standard model structure determining unit is configured to determine a Gaussian mixture distribution having an Mf (Mf≧1) number of mixture distributions as the structure of the standard model, and said statistics estimating unit is configured to calculate at least one of a mixture weighting coefficient ω f(m) (m=1,2,. .. , M f ), a mean value μ f(m) (m=1,2,. .. , M f ), and a variance σ f(m) 2 (m=1,2,. .. , M f ) which are the statistics of the standard model ∑ m = 1 M f ⁢ ω f ⁡ ( m ) ⁢ f ⁡ ( x ; μ f ⁡ ( m ) , σ f ⁡ ( m ) 2 ) (where f(x;μ f(m) ,σ f(m) 2 ) (m=1,2,. .. , M f ) represents a Gaussian distribution, and χ represents input data) represented by the Gaussian mixture distribution so as to maximize or locally maximize a likelihood log ⁢ ⁢ P = ∑ i = 1 N g ⁢ ∫ - ∞ ∞ ⁢ log ⁡ [ ∑ m = 1 M f ⁢ ω f ⁡ ( m ) ⁢ f ⁡ ( x ; μ f ⁡ ( m ) , σ f ⁡ ( m ) 2 ) ] ⁢ { ∑ i = 1 L g ⁡ ( i ) ⁢ υ g ⁡ ( i , l ) ⁢ g ⁡ ( x ; μ g ⁡ ( i , l ) , σ g ⁡ ( i , l ) 2 ) } ⁢ ⅆ x of the standard model, with respect to Ng (Ng≧2) reference models ∑ l = i L g ⁡ ( i ) ⁢ υ g ⁡ ( i , l ) ⁢ g ⁡ ( x ; μ g ⁡ ( i , l ) , σ g ⁡ ( i , l ) 2 ) ⁢ ⁢ ( i = 1 , 2 , … ⁢ , N g ) (where g(x;μ g(i,l) ,σ g(i,l) 2 ) (i=1,2,. .. , N g , l=1,2,. .. , L (i) ) represents a Gaussian distribution, L g(i) (i=1,2,. .. , N g ) represents a mixture distribution of each of reference models, ν g(i,l) (l=1,2,. .. , L g(i) ) represents a mixture weighting coefficient, μ 2 g(i,l) (l=1,2,. .. , L g(i) ) represents a mean value, and σ g(i,l) 2 (l=1,2,. .. , L g(i) ) represents a variance).