meeting. In this manner, the meeting-specific statistical language model 216 for a meeting can be updated with additional or modified meeting-specific data 214 prior to or during the meeting to further increase the accuracy of the generated transcription 112 of the meeting. For example, the meeting-specific statistical language model 216 for a meeting might be updated with meeting-specific data 214 associated with a new attendee 104 when a notification 316 is received indicating that the new attendee 104 has joined a meeting. The meeting-specific statistical language model 216 might be updated in a similar fashion following receipt of a notification 316 indicating that an attendee 104 has left a meeting. As discussed briefly above, schedule-based processing 306 and event-based processing 304 are both utilized to generate pre-meeting signals 212 in some configurations. [0052] FIG. 4 is a data structure diagram showing aspects of the configuration of an illustrative pre-meeting signal 212 used to trigger generation of a meeting-specific statistical language model 216, according to one embodiment disclosed herein. In the embodiment shown in FIG. 4, the pre-meeting signal 212 includes data 402A identifying the start time of a meeting, data 402B identifying a shard (i.e. a database) where information regarding the meeting can be found, data 402C specifying a unique identifier for the meeting, and data 402D containing a meeting-specific value. [0053] As will be described in greater detail below, the language model preparation service 210 can utilize the data contained in the pre-meeting signal 212 to obtain the meeting-specific data 214 for the meeting identified by the pre-meeting signal 212. Additional details regarding this process are provided below with regard to FIG. 5. [0054] FIG. 5 is a computing architecture diagram that shows additional aspects of the configuration and operation of the context-aware transcription system 102 shown in FIGS. 1-3 and described above for performing real-time meeting transcription using a meeting-specific statistical language model 216, according to one embodiment disclosed herein. As discussed briefly above, the receipt of a pre-meeting signal 212 triggers the language model preparation service 210 to generate a meeting-specific statistical language model 216 for the meeting identified by the pre-meeting signal 212. [0055] In order to generate the pre-meeting signal 212 for a meeting, the language model preparation service 212 uses the data contained in the pre-meeting signal 212 to locate and retrieve meeting-specific data 214 for the meeting from the substrate 206 and/or another location. As discussed above, the meeting-specific data 214 can include, but is not limited to, messages (e.g. email, text messages, instant messages, etc.) generated by or otherwise associated with meeting attendees 104, file attachments to messages, names of meeting