9 May 2018Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
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.
Kyongsik Yun,Joseph Osborne,Madison Lee,Thomas Lu, andEdward 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
ACCESS THE FULL ARTICLE
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Kyongsik Yun, Joseph Osborne, Madison Lee, Thomas Lu, 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