  Transcribing voice communications in NASA's launch control center is
important for information utilization. However, automatic speech recognition in
this environment is particularly challenging due to the lack of training data,
unfamiliar words in acronyms, multiple different speakers and accents, and
conversational characteristics of speaking. We used bidirectional deep
recurrent neural networks to train and test speech recognition performance. We
showed that data augmentation and custom language models can improve speech
recognition accuracy. Transcribing communications from the launch control
center will help the machine analyze information and accelerate knowledge
generation.
