10. A system comprising: one or more processors; and a non-transitory computer-readable medium coupled to the one or more computers having instructions stored thereon, which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving audio data encoding an utterance of a user; receiving context data associated with the received audio data; determining a likely context associated with a user, based on at least a portion of the context data; selecting one or more language model biasing parameters based at least on the likely context associated with the user; determining a context confidence score associated with the likely context based on at least a portion of the context data, and additional context data indicating (i) that the user has switched between applications, (ii) a time difference between a presentation of a search result and a user response to the presentation of the search result, (iii) gaze tracking data, or (iv) a user behavior in response to visible content; adjusting one or more of the language model biasing parameters based at least on the context confidence score; biasing a baseline language model based at least on one or more of the adjusted language model biasing parameters; providing the biased language model for use by an automated speech recognizer (ASR); generating a transcription of the received audio data using the biased language model; and transmitting the generated transcription for display on a client computing device.
10. A system comprising: one or more processors; and a non-transitory computer-readable medium coupled to the one or more computers having instructions stored thereon, which, when executed by the one or more computers, cause the one or more computers to perform operations comprising: receiving audio data encoding an utterance of a user; receiving context data associated with the received audio data; determining a likely context associated with a user, based on at least a portion of the context data; selecting one or more language model biasing parameters based at least on the likely context associated with the user; determining a context confidence score associated with the likely context based on at least a portion of the context data, and additional context data indicating (i) that the user has switched between applications, (ii) a time difference between a presentation of a search result and a user response to the presentation of the search result, (iii) gaze tracking data, or (iv) a user behavior in response to visible content; adjusting one or more of the language model biasing parameters based at least on the context confidence score; biasing a baseline language model based at least on one or more of the adjusted language model biasing parameters; providing the biased language model for use by an automated speech recognizer (ASR); generating a transcription of the received audio data using the biased language model; and transmitting the generated transcription for display on a client computing device. 14. The system of claim 10 , wherein the baseline language model indicates scores associated with different n-gram sequences.