In the field of spoken language understanding, systems like Whisper and
Multilingual Massive Speech (MMS) have shown state-of-the-art performances.
This study is dedicated to a comprehensive exploration of the Whisper and MMS
systems, with a focus on assessing biases in automatic speech recognition (ASR)
inherent to casual conversation speech specific to the Portuguese language. Our
investigation encompasses various categories, including gender, age, skin tone
color, and geo-location. Alongside traditional ASR evaluation metrics such as
Word Error Rate (WER), we have incorporated p-value statistical significance
for gender bias analysis. Furthermore, we extensively examine the impact of
data distribution and empirically show that oversampling techniques alleviate
such stereotypical biases. This research represents a pioneering effort in
quantifying biases in the Portuguese language context through the application
of MMS and Whisper, contributing to a better understanding of ASR systems'
performance in multilingual settings.

本研究旨在全面探索 Whisper 和 MMS 系统，重点评估与葡萄牙语的非正式对话语音相关的自动语音识别中存在的偏见。我们的调查包含不同类别，包括性别、年龄、肤色和地理位置。除了传统的 ASR 评估指标（如词错误率 WER）外，我们还引入了 p 值统计显著性来分析性别偏见。此外，我们广泛研究了数据分布的影响，并实证表明过采样技术可以减轻此类刻板化偏见。本研究通过应用 MMS 和 Whisper 在葡萄牙语环境中量化偏见，对多语言设置下 ASR 系统性能有了更好的理解，是一项开创性的工作。

平衡的行动：揭示和缓解葡萄牙语 ASR 偏见

The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese

We consider the problem of fairly allocating indivisible goods, among agents,
under cardinality constraints and additive valuations. In this setting, we are
given a partition of the entire set of goods---i.e., the goods are
categorized---and a limit is specified on the number of goods that can be
allocated from each category to any agent. The objective here is to find a fair
allocation in which the subset of goods assigned to any agent satisfies the
given cardinality constraints. This problem naturally captures a number of
resource-allocation applications, and is a generalization of the well-studied
(unconstrained) fair division problem.
The two central notions of fairness, in the context of fair division of
indivisible goods, are envy freeness up to one good (EF1) and the (approximate)
maximin share guarantee (MMS). We show that the existence and algorithmic
guarantees established for these solution concepts in the unconstrained setting
can essentially be achieved under cardinality constraints. Specifically, we
develop efficient algorithms which compute EF1 and approximately MMS
allocations in the constrained setting.
Furthermore, focusing on the case wherein all the agents have the same
additive valuation, we establish that EF1 allocations exist and can be computed
efficiently even under laminar matroid constraints.

本论文主要研究如何在给定的特定限制条件下，实现资源公平配置问题，提出了一种有效的算法来计算两个中心公平概念，即 EF1 和 MMS，在满足限制条件的状态下，特别地，证明了在所有代理具有相同加性估值的情况下，即使存在限制条件，仍然可以有效地计算出 EF1 分配。