Automatic Speech Recognition (ASR) systems have become ubiquitous. They can be found in a variety of form factors and are increasingly important in our daily lives. As such, ensuring that these systems are equitable to different subgroups of the population is crucial. In this paper, we introduce, AequeVox, an automated testing framework for evaluating the fairness of ASR systems. AequeVox simulates different environments to assess the effectiveness of ASR systems for different populations. In addition, we investigate whether the chosen simulations are comprehensible to humans. We further propose a fault localization technique capable of identifying words that are not robust to these varying environments. Both components of AequeVox are able to operate in the absence of ground truth data. We evaluated AequeVox on speech from four different datasets using three different commercial ASRs. Our experiments reveal that non-native English, female and Nigerian English speakers generate 109%, 528.5% and 156.9% more errors, on average than native English, male and UK Midlands speakers, respectively. Our user study also reveals that 82.9% of the simulations (employed through speech transformations) had a comprehensibility rating above seven (out of ten), with the lowest rating being 6.78. This further validates the fairness violations discovered by AequeVox. Finally, we show that the non-robust words, as predicted by the fault localization technique embodied in AequeVox, show 223.8% more errors than the predicted robust words across all ASRs.
翻译:自动语音识别( ASR) 系统已经变得无处不在。 它们可以以多种形式出现, 在我们的日常生活中越来越重要。 因此, 确保这些系统对不同的人口分组公平至关重要 。 在本文中, 我们引入了 AequeVox, 一个用于评估 ASR 系统的公正性的自动测试框架。 AequeVox 模拟不同的环境来评估 ASR 系统对不同人群的有效性。 此外, 我们调查所选的模拟是否为人类所理解。 我们还提出了一种错误本地化技术, 能够识别不适应这些不同环境的词。 因此, AequeVox 的两个组成部分都能够在没有地面真实数据的情况下运行。 我们用三种不同的商业 ASR 来评估四个不同的数据集的语音上的 AequeVox 。 我们的实验显示, 非本地性英语、女性和尼日利亚英语语言的生成了109%、 528.5% 和156.9 % 的错误, 平均比本地英语、 男性和英国中地语言的错误。 我们的用户研究还显示, 我们的模拟82.9% 的准确性( 78 通过10级的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正的校正) 。