Apollo 11 was the first manned space mission to successfully bring astronauts to the Moon and return them safely. As part of NASA’s goal in assessing team and mission success, all voice communications within mission control, astronauts, and support staff were captured using a multichannel analog system, which until recently had never been made available. More than 400 personnel served as mission specialists/support who communicated across 30 audio loops, resulting in 9,000+ h of data. It is essential to identify each speaker’s role during Apollo and analyze group communication to achieve a common goal. Manual annotation is costly, so this makes it necessary to determine robust speaker identification and tracking methods. In this study, a subset of 100hr derived from the collective 9,000hr of the Fearless Steps (FSteps) Apollo 11 audio data were investigated, corresponding to three critical mission phases: liftoff, lunar landing, and lunar walk. A speaker recognition assessment is performed on 140 speakers from a collective set of 183 NASA mission specialists who participated, based on sufficient training data obtained from 5 (out of 30) mission channels. We observe that SincNet performs the best in terms of accuracy and F score achieving 78.6% accuracy. Speaker models trained on specific phases are also compared with each other to determine if stress, g-force/atmospheric pressure, acoustic environments, etc., impact the robustness of the models. Higher performance was obtained using i-vector and x-vector systems for phases with limited data, such as liftoff and lunar walk. When provided with a sufficient amount of data (lunar landing phase), SincNet was shown to perform the best. This represents one of the first investigations on speaker recognition for massively large team-based communications involving naturalistic communication data. In addition, we use the concept of “Where’s Waldo?” to identify key speakers of interest (SOIs) and track them over the complete FSteps audio corpus. This additional task provides an opportunity for the research community to transition the FSteps collection as an educational resource while also serving as a tribute to the “heroes behind the heroes of Apollo.”
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This content will become publicly available on August 20, 2024
Speaker Tracking using Graph Attention Networks with Varying Duration Utterances across Multi-Channel Naturalistic Data: Fearless Steps Apollo-11 Audio Corpus
Speaker tracking in spontaneous naturalistic data continues to be a major research challenge, especially for short turn-taking communications. The NASA Apollo-11 space mission brought astronauts to the moon and back, where team based voice communications were captured. Building robust speaker classification models for this corpus has significant challenges due to variability of speaker turns, imbalanced speaker classes, and time-varying background noise/distortions. This study proposes a novel approach for speaker classification and tracking, utilizing a graph attention network framework that builds upon pretrained speaker embeddings. The model’s robustness is evaluated on a number of speakers (10-140), achieving classification accuracy of 90.78% for 10 speakers, and 79.86% for 140 speakers. Furthermore, a secondary investigation focused on tracking speakers-of-interest(SoI) during mission critical phases, essentially serves as a lasting tribute to the 'Heroes Behind the Heroes'.
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- Award ID(s):
- 2016725
- NSF-PAR ID:
- 10484454
- Publisher / Repository:
- ISCA
- Date Published:
- Journal Name:
- ISCA INTERSPEECH-2023
- Page Range / eLocation ID:
- 1459 to 1463
- Subject(s) / Keyword(s):
- ["speaker classification","graph attention networks","speaker tracking","graph neural networks"]
- Format(s):
- Medium: X
- Location:
- Dublin, Ireland
- Sponsoring Org:
- National Science Foundation
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