4. The method of claim 2 , further comprising: maximizing a combination of the probabilities by repeating the determination of at least one of the weights in the set of n-gram language model weights λ M , the determination of at least one of the weights in the set of sentence cluster weights γ C , and the probabilities p w of the sentences in the set of development sentences W.
4. The method of claim 2 , further comprising: maximizing a combination of the probabilities by repeating the determination of at least one of the weights in the set of n-gram language model weights λ M , the determination of at least one of the weights in the set of sentence cluster weights γ C , and the probabilities p w of the sentences in the set of development sentences W. 6. The method of claim 4 , wherein: each of the n-gram language model weights λ m from n-gram language model weights λ M satisfies λ c , m ′ = 1  c  ⁢ ∑ w ∈ c ⁢ ⁢ ∑ i = 1  w  ⁢ ⁢ λ c , m ⁢ p m ⁡ ( w i ❘ h i ) ∑ j = 1 M ⁢ ⁢ λ c , j ⁢ p j ⁡ ( w j ❘ h j ) , and each of the sentence cluster weights γ c from the set of sentence cluster weights γ C satisfies γ c =  c   W  , where c is one of the clusters in the set of sentence clusters, |c| is the quantity of sentences in cluster c, m is a language model from the set of two or more language models G M , M is the quantity of language models in the set of two or more language models G M , |W| is the quantity of sentences in set of development sentences W, |w| is the quantity of n-grams in the sentence w from the set of development sentences W, and p m (w i |h i ) is the probability that n-gram w i follows history h i as estimated by a language model G m from the set of two or more language models G M .