1. A computer-implemented method 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; and 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.