10. A non-transitory computer-readable storage device having instructions stored thereon that, when executed by a computing device of an automated speech recognition system, cause the computing device to perform speech recognition operations comprising: receiving, by the computing device of the automated speech recognition system, audio data and context data associated with the audio data, wherein the received context data indicates a current context of a device when the device captured the audio data; identifying, by the computing device of the automated speech recognition system, a topic based on a comparison of the received context data with second context data indicating a previous context of a device, wherein identifying the topic includes: selecting, by the computing device of the automated speech recognition system, a term based on the comparison between the received context data and the second context data; and in response to selecting the term, selecting, by the computing device of the automated speech recognition system and as the identified topic, a topic that is associated with the selected term; based on identifying the topic, adjusting, by the computing device of the automated speech recognition system, a language model based on the identified topic to adjust a likelihood that the language model indicates for one or more terms associated with the topic; determining, by the computing device of the automated speech recognition system, a transcription of the audio data using the adjusted language model; and outputting, by the computing device of the automated speech recognition system, the transcription determined using the adjusted language model, the transcription being output as a speech recognition output of the automated speech recognition system.
12. The non-transitory computer-readable storage device of claim 10 , wherein adjusting the language model based on the identified topic to adjust the likelihood that the language model indicates for one or more terms associated with the topic includes: adjusting the language model based on the identified topic to adjust a likelihood that the language model indicates for one or more terms associated with the topic that are different from the selected term.