10. A method executable by one or more computing processors to perform operations comprising: identify a plurality of n-grams included in user information associated with at least a portion of a digital work, an n-gram of the plurality of n-grams comprising a sequence of one or more words; associating a first probability of occurrence with the n-gram based at least in part on a frequency of occurrence of the n-gram in at least the user information; determining language model difference information based at least in part on the first probability of occurrence associated with the n-gram differs from a second probability of occurrence of the n-gram in a base language model by more than a threshold amount; and determining a word based at least in part on a captured utterance, the base language model, and the language model difference information.
10. A method executable by one or more computing processors to perform operations comprising: identify a plurality of n-grams included in user information associated with at least a portion of a digital work, an n-gram of the plurality of n-grams comprising a sequence of one or more words; associating a first probability of occurrence with the n-gram based at least in part on a frequency of occurrence of the n-gram in at least the user information; determining language model difference information based at least in part on the first probability of occurrence associated with the n-gram differs from a second probability of occurrence of the n-gram in a base language model by more than a threshold amount; and determining a word based at least in part on a captured utterance, the base language model, and the language model difference information. 14. The method as recited in claim 10 , further comprising generating the base language model based at least in part on at least one of a webpage, an electronic book, a news feed, a social network site, a microblog, or a closed captioning feed.