Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model

Kyongsik Yun; Joseph Osborne; Madison Lee; Thomas Lu; Edward Chow

doi:10.1117/12.2304569

9 May 2018 Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model

Kyongsik Yun, Joseph Osborne, Madison Lee, Thomas Lu, Edward Chow

Author Affiliations +

Proceedings Volume 10652, Disruptive Technologies in Information Sciences; 1065202 (2018) https://doi.org/10.1117/12.2304569
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

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.

Conference Presentation

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Kyongsik Yun, Joseph Osborne, Madison Lee, Thomas Lu, and Edward Chow "Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model", Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 1065202 (9 May 2018); https://doi.org/10.1117/12.2304569

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