Automatic speech recognition (ASR) services are ubiquitous, transforming speech into text for systems like Amazon's Alexa, Google's Assistant, and Microsoft's Cortana. However, researchers have identified biases in ASR performance between particular English language accents by racial group and by nationality. In this paper, we expand this discussion both qualitatively by relating it to historical precedent and quantitatively through a large-scale audit. Standardization of language and the use of language to maintain global and political power have played an important role in history, which we explain to show the parallels in the ways in which ASR services act on English language speakers today. Then, using a large and global data set of speech from The Speech Accent Archive which includes over 2,700 speakers of English born in 171 different countries, we perform an international audit of some of the most popular English ASR services. We show that performance disparities exist as a function of whether or not a speaker's first language is English and, even when controlling for multiple linguistic covariates, that these disparities have a statistically significant relationship to the political alignment of the speaker's birth country with respect to the United States' geopolitical power.

翻译：自动语音识别(ASR)服务无处不在,将言论转化为亚马孙亚历克萨、谷歌助理和微软科塔纳等系统的文本。然而,研究人员在ASR表现中发现种族群体和国籍在特定英语口音之间的偏差。在本文中,我们通过将其与历史先例联系起来和通过大规模审计从质量上扩大这一讨论,在数量上将其与历史先例联系起来。语言标准化和使用语言来维持全球和政治权力在历史上发挥了重要作用,我们解释说,ASR服务在今天英语使用者问题上的表现与ASR服务相似。然后,利用来自Acent Acent档案馆的大型全球数据集,其中包括171个不同国家出生的2 700多名英语发言者,我们对一些最受欢迎的英语ASR服务进行国际审计。我们表明,表现差异在于是否一个发言者的第一语言是英语,而且即使控制多种语言变换,这些差异在统计上与发言者的出生国与美国地缘政治力量的政治调整有着重要关系。