The Fearless Steps APOLLO Community Resource provides unparalleled
opportunities to explore the potential of multi-speaker team communications
from NASA Apollo missions. This study focuses on discovering the
characteristics that make Apollo recordings more or less intelligible to
Automatic Speech Recognition (ASR) methods. We extract, for each audio
recording, interpretable metadata on recordings (signal-to-noise ratio,
spectral flatness, presence of pauses, sentence duration), transcript (number
of words spoken, speaking rate), or known a priori (speaker). We identify
subgroups of audio recordings based on combinations of these metadata and
compute each subgroup's performance (e.g., Word Error Rate) and the difference
in performance (''divergence'') w.r.t the overall population. We then apply the
Whisper model in different sizes, trained on English-only or multilingual
datasets, in zero-shot or after fine-tuning. We conduct several analyses to (i)
automatically identify and describe the most problematic subgroups for a given
model, (ii) examine the impact of fine-tuning w.r.t. zero-shot at the subgroup
level, (iii) understand the effect of model size on subgroup performance, and
(iv) analyze if multilingual models are more sensitive than monolingual to
subgroup performance disparities. The insights enhance our understanding of
subgroup-specific performance variations, paving the way for advancements in
optimizing ASR systems for Earth-to-space communications.

该研究探讨了 NASA 阿波罗任务的多话者团队通信潜力，并提取了音频记录的关键元数据，通过分析不同元数据组合的子组的性能差异，研究了自动语音识别方法在阿波罗录音中的可行性和问题，在不同模型和多语言处理上优化了 ASR 系统的性能，为地球与太空通信的 ASR 系统优化提供了理论基础。