specific data 214, this service 210 utilizes the meeting-specific data 214 to generate a meeting-specific statistical language model 216. In some configurations, the meeting- specific statistical language model 216 is stored in the substrate 206 during the duration of the meeting for resiliency purposes and deleted after the end of the meeting. [0043] The meeting-specific statistical language model 216 defines a probability distribution that can be used to predict the next word in a sequence given the words that proceed it. Such a language model learns the probability of word occurrence based on examples of text, such as the meeting-specific data 214. In this way, the meeting-specific statistical language model can be used to determine probabilities of spoken words in the audio and to generate a transcription based upon the computed probabilities. [0044] Various technologies can be used to implement the meeting-specific statistical language model 216 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, long short term memory (“LSTM”) models, or recurrent neural network language models. [0045] In some configurations, the language model preparation service 210 generates the meeting-specific statistical language model 216 in response to receiving a pre meeting signal 212 generated by a schedule processor and trigger service (“STS”) 302 (shown in FIG. 3). As will be described in greater detail below, the pre-meeting signal 212 can include data describing a meeting such as, for example, data identifying the meeting and data identifying a start time of the meeting. Details regarding the generation of the pre meeting signal 212 are provided below with regard to FIGS 3 and 4. [0046] As will also be described in greater detail below, a speech transcription service 502 (shown in FIG. 5) can utilize the meeting-specific statistical language model 216 to generate a text transcription 112 of the audio 114 of a meeting. In some configurations, the speech transcription service 502 utilizes the meeting-specific statistical language model 216 in conjunction with a base (i.e. non- domain-specific) language model to generate transcriptions 112. Details regarding the operation of the speech transcription service 502 will be provided below with regard to FIG. 5. [0047] FIG. 3 is a computing architecture diagram that shows aspects of the configuration and operation of the context-aware transcription system 102 shown in FIGS. 1 and 2 for generating a pre-meeting signal 212 that triggers the generation of a meeting- specific statistical language model 216, according to one embodiment disclosed herein. As