1. A system for discriminatively pruning a language model, the system comprising: an electronic data store configured to store a corpus of training texts; and a computing device in communication with the electronic data store, the computing device configured to: obtain a confusion matrix of confusable phonemes; for a first text of the corpus of training texts, compute a first word lattice comprising the first text and an alternative hypothesis for the first text, wherein the first text comprises a first word; for a second text of the corpus of training texts, compute a second word lattice comprising the second text and an alternative hypothesis for the second text using the confusion matrix, wherein the second text comprises a second word, and wherein the alternative hypothesis for the second text comprises the second text with the first word substituted for the second word; obtain a language model comprising a plurality of trigrams; for a first trigram of the plurality of trigrams, wherein the first trigram comprises the first word in a context of two other words, determine a plurality of values using the language model without pruning the first trigram, the plurality of values comprising: a trigram probability for the first trigram; a backoff probability for the first trigram, wherein the backoff probability is computed using a backoff weight and a bigram probability, and wherein the backoff probability corresponds to a probability used in the absence of the trigram probability; a true path probability that a first true path of the first word lattice is correct, wherein the first true path comprises the first text; and an error path probability that a first error path of the second word lattice is correct, wherein the first error path the alternative hypothesis for the second text; compute a discriminative objective function value using the plurality of values, wherein the discriminative objective function value is based at least partly on a difference between (i) a first sum of values computed for individual true paths including the first true path, and (ii) a second sum of values computed for individual error paths including the first error path, wherein the value computed for the first true path is computed using the true path probability, the tri-gram probability and the backoff probability, and wherein the value computed for the first error path is computed using the error path probability, the tri-gram probability and the backoff probability; based at least in part on the discriminative objective function value, prune the first trigram from the language model to generate a pruned language model; receive, from a user computing device, an audio signal corresponding to speech of a user; and recognize the speech, via a speech recognition server, using the pruned language model.