11. A computer-implemented method comprising: under control of one or more computing devices configured with specific computer-executable instructions, generating a lattice comprising a plurality of recognition hypotheses for acoustic model training data, wherein the acoustic model training data is associated with a transcription, wherein the transcription comprises a sequence of one or more transcribed subword units, wherein a first path through the lattice comprises a first sequence of one or more recognized subword units, wherein each transcribed subword unit of the sequence of transcribed subword units is one of a plurality of language subword units, and wherein each recognized subword unit of the first sequence of recognized subword units is one of the plurality of language subword units; calculating a substitution probability that a first language subword unit is substituted by a second language subword unit based at least in part on a comparison of the first sequence of one or more recognized subword units to the sequence of one or more transcribed subword units; generating a confusion matrix using the substitution probability, wherein the confusion matrix comprises a plurality of substitution probabilities; updating language model training data to generate updated language model training data, wherein updating the language model training data comprises adding an alternate transcription based at least partly on the transcription and the confusion matrix; and discriminatively training a language model using the updated language model training data.
20. The computer-implemented method of claim 11 , wherein the plurality of substitution probabilities comprises, for each combination of two different language subword units of the plurality of language subword units, a corresponding substitution probability that one language subword unit of the two different language subword units will be confused with another language subword unit of the two different language subword units.