OK Aura，公平对待我：针对唤醒词检测中偏见缓解的去人口统计学训练
"OK Aura, Be Fair With Me": Demographics-Agnostic Training for Bias Mitigation in Wake-up Word Detection
AI总结：
本文探讨了去人口统计学训练方法在缓解唤醒词检测中偏见问题的效果，通过数据增强和知识蒸馏提升模型泛化能力，实验显示该方法显著减少性别、年龄和口音的偏见差异。
摘要：
基于语音的界面被广泛应用；然而，实现公平的唤醒词检测以应对多样化说话人群仍是一个关键挑战，由于持续存在的种族偏见。本文评估了去人口统计学训练技术在缓解不同性别、年龄和口音说话者之间性能差异的有效性。我们使用OK Aura数据库进行实验，采用一种排除人口统计学标签的训练方法，这些标签仅用于评估目的。我们探索（i）数据增强技术以提高模型泛化能力，以及（ii）预训练基础语音模型的知识蒸馏。实验结果表明，这些去人口统计学训练技术显著减少了种族偏见，导致不同说话群体之间更公平的性能分布。具体而言，其中一种评估技术在与基线相比时，性别预测不平等减少了39.94%，年龄减少了83.65%，口音减少了40.48%。本研究突显了标签无关方法在促进唤醒词检测公平性方面的有效性。
英文摘要：
Voice-based interfaces are widely used; however, achieving fair Wake-up Word detection across diverse speaker populations remains a critical challenge due to persistent demographic biases. This study evaluates the effectiveness of demographics-agnostic training techniques in mitigating performance disparities among speakers of varying sex, age, and accent. We utilize the OK Aura database for our experiments, employing a training methodology that excludes demographic labels, which are reserved for evaluation purposes. We explore (i) data augmentation techniques to enhance model generalization and (ii) knowledge distillation of pre-trained foundational speech models. The experimental results indicate that these demographics-agnostic training techniques markedly reduce demographic bias, leading to a more equitable performance profile across different speaker groups. Specifically, one of the evaluated techniques achieves a Predictive Disparity reduction of 39.94\% for sex, 83.65\% for age, and 40.48\% for accent when compared to the baseline. This study highlights the effectiveness of label-agnostic methodologies in fostering fairness in Wake-up Word detection.
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。
