Share Email Print
cover

Proceedings Paper

Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model
Author(s): Kyongsik Yun; Joseph Osborne; Madison Lee; Thomas Lu; Edward Chow
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 9 May 2018
PDF: 7 pages
Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 1065202 (9 May 2018); doi: 10.1117/12.2304569
Show Author Affiliations
Kyongsik Yun, Jet Propulsion Lab. (United States)
Joseph Osborne, Jet Propulsion Lab. (United States)
Madison Lee, Jet Propulsion Lab. (United States)
Thomas Lu, Jet Propulsion Lab. (United States)
Edward Chow, Jet Propulsion Lab. (United States)


Published in SPIE Proceedings Vol. 10652:
Disruptive Technologies in Information Sciences
Misty Blowers; Russell D. Hall; Venkateswara R. Dasari, Editor(s)

© SPIE. Terms of Use
Back to Top