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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.” 

Apollo-11 was the first manned space mission to successfully bring astronauts to the moon. More than + 400 mission specialists/support team members were involved whose voice communications were captured using the SoundScriber multi-channel analog system. To ensure mission success, it was necessary for teams to engage, communicate, learn, address and solve problems in a timely manner. Hence, in order to identify each speaker’s role during Apollo missions and analyze group communication, we need to automatically tag and track speakers individually since manual annotation is costly and time consuming on a massive audio corpus. In this study, we focus on a subset of 100 h derived from the 10 000 h of the Fearless Steps Apollo-11 audio data. We use the concept of “Where’s Waldo” to identify all instances of our speakers-of-interest: (i) Three Astronauts; (ii) Flight Director; and (iii) Capsule Communicator. Analyzing the handful of speakers present in the small audio dataset of 100 h can be extended to the complete Apollo mission. This analysis provides an opportunity to recognize team communications, group dynamics, and human engagement/psychology. Identifying these personnel can help pay tribute to the hundreds of notable engineers and scientists who made this scientific accomplishment possible. Sponsored by NSF #2016725 

The Fearless Steps Challenge (FSC) initiative was designed to host a series of progressively complex tasks to promote advanced speech research across naturalistic “Big Data” corpora. The Center for Robust Speech Systems at UT-Dallas in collaboration with the National Institute of Standards and Technology (NIST) and Linguistic Data Consortium (LDC) conducted Phase-3 of the FSC series (FSC P3), with a focus on motivating speech and language technology (SLT) system generalizability across channel and mission diversity under the same training conditions as in Phase-2. The FSC P3 introduced 10 hours of previously unseen channel audio from Apollo-11 and 5 hours of novel audio from Apollo-13 to be evaluated over both previously established and newly introduced SLT tasks with streamlined tracks. This paper presents an overview of the newly introduced conversational analysis tracks, Apollo-13 data, and analysis of system performance for matched and mismatched challenge conditions. We also discuss the Phase-3 challenge results, evolution of system performance across the three Phases, and next steps in the Challenge Series.