attendees 104, or files created by or otherwise associated with meeting attendees 104. The meeting-specific data 214 can include jargon, acronyms, or other types of domain-specific terminology associated with the meeting attendees 104. [0056] Once the language model preparation service 210 has generated the meeting- specific statistical model 216, the language model preparation service 210 can store the meeting-specific statistical language model 216 in the substrate 206 or at another location. As discussed above, the meeting-specific statistical language model 216 can be stored during the duration of a meeting for resiliency purposes and destroyed after the end of the meeting in some configurations. [0057] As shown in FIG. 5, the language model preparation service 210 also provides the meeting-specific statistical language model 216 to the speech transcription service 502. As described briefly above, the speech transcription service 502 is a network service configured to receive the audio 114 of a meeting and to generate a text transcription 112 of the speech contained in the audio 114. In order to provide this functionality, a multiplexor 504 is utilized in one configuration to receive the audio 114 of a meeting from the participating computing devices 106. The multiplexor 504 provides the audio 114 to the speech transcription service 502. As shown in FIG. 5, the multiplexor 504 can also provide the audio 114 to each of the participating computing devices 106. [0058] The speech transcription service 502 receives the audio 114 and utilizes the meeting-specific statistical language model 216 to generate a transcription 112 of the audio 114 in real time (i.e. as the attendees 104 speak). The speech transcription service 502 utilizes the meeting-specific statistical language model 216 in conjunction with a base statistical language model 506 in some configurations. The meeting-specific statistical language model 216 includes domain-specific (i.e. meeting-specific) terms, while the base statistical language model 506 typically does not include domain-specific terminology. [0059] As with the meeting-specific statistical language model 216, the base statistical language model 506 also defines a probability distribution that can be used to predict the next word in a sequence given the words that proceed it. Various technologies can be used to implement the base statistical language model 506 including, but not limited to, incremental language models, n-gram language models, uniform distribution (zero- gram) language models, unigram language models, bigram language models, skip language models, class language models, topic language models, neural network language models, or recurrent neural network language models. [0060] The speech transcription service 502 provides the transcription 112 to the