平衡之举：揭示并缓解葡萄牙语中的ASR偏见
The Balancing Act: Unmasking and Alleviating ASR Biases in Portuguese
AI总结：
本研究全面评估了Whisper和MMS在葡萄牙语日常对话ASR中的性别、年龄等偏见，引入p值统计分析，并证明过采样技术能有效缓解此类刻板偏见。
摘要：
在口语理解领域，Whisper和Multilingual Massive Speech（MMS）等系统已展现出最先进的性能。本研究致力于全面探索Whisper和MMS系统，重点评估葡萄牙语特定日常对话语音中固有的自动语音识别（ASR）偏见。我们的调查涵盖多个类别，包括性别、年龄、肤色和地理位置。除了词错误率（WER）等传统ASR评估指标外，我们还引入了用于性别偏见分析的p值统计显著性。此外，我们广泛考察了数据分布的影响，并经验性地证明过采样技术能缓解此类刻板偏见。本研究是通过应用MMS和Whisper来量化葡萄牙语语境中偏见的开创性努力，有助于更好地理解ASR系统在多语言环境中的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
