1. In a language model employing a classing function defining classes of words, each of the classes grouping words sharing a similar likelihood of appearing in a production application context, a method of optimizing the classing function comprising: identifying a language context corresponding to a production application, the language context based on usage encountered by a language model invoked by the production application; defining a training corpus having a set of clusters indicative of expected usage, the clusters being n-grams having a sequence of n words for defining a probability that the first n−1 words in the sequence is followed by word n in the sequence; and building a language model from a classing function applied to the training corpus, the classing function optimized to correspond to usage in the identified language context using class-based and word-based features by computing a likelihood of a word in an n-gram and a frequency of a word within a class of the n-gram, optimizing the classing function further comprising: employing a word based classing approach; backing off, if the word based approach indicates a null probability; and employing a class based approach; further comprising: determining seen and unseen clusters, the unseen clusters having a previously unoccurring sequence of words; employing the word based classification if the cluster has a previous occurrence, identifying a discount parameter, the discount parameter reducing a count of word occurrences of a particular cluster in favor of a class count of words of the cluster; backing off using the discount parameter and employing a class based approach if the cluster is unseen, unseen clusters based on occurrence of any of the words in the cluster, the unseen cluster having a nonzero probability if any word in the class of words has occurred; the discount parameter reducing a count of word occurrences of a particular cluster in favor of a class count of words of the cluster; and the discount parameter defining an absolute discounting model, further comprising: identify a discount parameter indicative of a reduction of a word count of words in a cluster; determining if the cluster is to be pruned or retained in the corpus; subtracting the discount parameter from a maximum count of the observed word based count of the cluster to compute a discounted count; or defining the discount count of the cluster as zero if the cluster is pruned.
1. In a language model employing a classing function defining classes of words, each of the classes grouping words sharing a similar likelihood of appearing in a production application context, a method of optimizing the classing function comprising: identifying a language context corresponding to a production application, the language context based on usage encountered by a language model invoked by the production application; defining a training corpus having a set of clusters indicative of expected usage, the clusters being n-grams having a sequence of n words for defining a probability that the first n−1 words in the sequence is followed by word n in the sequence; and building a language model from a classing function applied to the training corpus, the classing function optimized to correspond to usage in the identified language context using class-based and word-based features by computing a likelihood of a word in an n-gram and a frequency of a word within a class of the n-gram, optimizing the classing function further comprising: employing a word based classing approach; backing off, if the word based approach indicates a null probability; and employing a class based approach; further comprising: determining seen and unseen clusters, the unseen clusters having a previously unoccurring sequence of words; employing the word based classification if the cluster has a previous occurrence, identifying a discount parameter, the discount parameter reducing a count of word occurrences of a particular cluster in favor of a class count of words of the cluster; backing off using the discount parameter and employing a class based approach if the cluster is unseen, unseen clusters based on occurrence of any of the words in the cluster, the unseen cluster having a nonzero probability if any word in the class of words has occurred; the discount parameter reducing a count of word occurrences of a particular cluster in favor of a class count of words of the cluster; and the discount parameter defining an absolute discounting model, further comprising: identify a discount parameter indicative of a reduction of a word count of words in a cluster; determining if the cluster is to be pruned or retained in the corpus; subtracting the discount parameter from a maximum count of the observed word based count of the cluster to compute a discounted count; or defining the discount count of the cluster as zero if the cluster is pruned. 2. The method of claim 1 further comprising defining a classing function for generating a custom language model for customizing classes to a particular production application by invoking a smoothing function for learning classes customized to a final model in system, the custom language model being optimized toward a production system by backing off the word based approach and employing a class based approach by invoking a combination of word and class n-gram features.