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。

休斯顿，我们遇到了分歧：ASR 模型的子群绩效分析

Houston we have a Divergence: A Subgroup Performance Analysis of ASR  Models

Deep metric learning (DML) enables learning with less supervision through its
emphasis on the similarity structure of representations. There has been much
work on improving generalization of DML in settings like zero-shot retrieval,
but little is known about its implications for fairness. In this paper, we are
the first to evaluate state-of-the-art DML methods trained on imbalanced data,
and to show the negative impact these representations have on minority subgroup
performance when used for downstream tasks. In this work, we first define
fairness in DML through an analysis of three properties of the representation
space -- inter-class alignment, intra-class alignment, and uniformity -- and
propose finDML, the fairness in non-balanced DML benchmark to characterize
representation fairness. Utilizing finDML, we find bias in DML representations
to propagate to common downstream classification tasks. Surprisingly, this bias
is propagated even when training data in the downstream task is re-balanced. To
address this problem, we present Partial Attribute De-correlation (PARADE) to
de-correlate feature representations from sensitive attributes and reduce
performance gaps between subgroups in both embedding space and downstream
metrics.

本文首次针对非平衡数据进行深度度量学习（DML）方法的公平性评估，提出了 finDML 工具来表征 DML 的公平性并发现了 DML 的子集表现的表示偏差，而 Partial Attribute De-correlation (PARADE) 则减少了对敏感属性的相关性并减小了表征空间中的性能差距。