23. A non-transitory computer-readable medium having a computer-executable component, the computer-executable component being configured to: obtain a confusion probability that a first phoneme is recognized as a second phoneme; obtain a first text comprising a first word; obtain a second text comprising a second word; generate, using the confusion probability, an erroneous hypothesis for the second text, wherein generating the erroneous hypothesis comprises substituting the first word for the second word in the second text; for a first n-gram of a language model, wherein the first n-gram comprises the first word in a context of one or more other words, determine a plurality of values using the language model, wherein the language model comprises the first n-gram, the plurality of values comprising: an n-gram probability for the first n-gram; a backoff probability for the first n-gram; a first probability that a true hypothesis comprising the first text is correct; and a second probability that the erroneous hypothesis is correct; compute an objective function value using the plurality of values, wherein the objective function value is based at least partly on a difference (i) a first sum of values computed for individual true hypotheses including the true hypothesis, and (ii) a second sum of values computed for individual erroneous hypotheses including the erroneous hypothesis, wherein the value computed for the true hypothesis is computed using the first probability, the n-gram probability and the backoff probability, and wherein the value computed for the erroneous hypothesis is computed using the second probability, the n-gram probability, and the backoff probability; based at least in part on the objective function value, prune the first n-gram from the language model to generate a pruned language model; and recognize user speech using the pruned language model and an audio signal corresponding to speech of a user.
30. The non-transitory computer-readable medium of claim 23 , wherein the value computed for the true hypothesis is computed by multiplying the first probability by a difference of (i) a logarithm of the n-gram probability and (ii) a logarithm of the backoff probability.