11. The computer-implemented method of claim 4 , further comprising analyzing content catalog information associated with a plurality of user profiles in comparison with the content catalog information associated with the user profile.
4. A computer-implemented method comprising: under control of one or more computing devices configured to execute specific instructions, obtaining content catalog information specific to a user profile; generating a personal language model based at least on the content catalog information, wherein the personal language model is specific to the user profile, wherein the personal language model includes a first subset of items in a general language model, and wherein the general language model is not associated with any specific user profile; determining, based at least on the content catalog information, predicted utterance content, wherein the content catalog information does not include the predicted utterance content; generating a predictive language model based at least on the predicted utterance content, wherein the predictive language model is specific to the user profile, and wherein the predictive language model includes a second subset of items in the general language model; generating a weighting factor that, when applied to the general language model, reduces probabilities associated with at least a portion of items in the general language model that are determined to be acoustically confusable for at least: a portion of items of the predicted utterance content, or a portion of items of the content catalog information; recognizing a first item in a first utterance using the general language model in combination with the personal language model and the predictive language model, wherein the first item is recognized based at least partly on a first probability for the first item being higher than a second probability for a second item and a third probability for a third item, wherein a value of the first probability comprises a probability value from of the personal language model, wherein a value of the second probability comprises a probability value from the predictive language model, and wherein a value of the third probability comprises a product of the weighting factor and a probability value from the general language model; and causing audio content associated with the first item to be played by an output device.