Performance Disparities Between Accents in Automatic Speech Recognition

Alex DiChristofano, Henry Shuster, Shefali Chandra, Neal Patwari

Keywords: bias, automatic speech recognition, natural language processing, artificial intelligence, machine learning, accent, dialect, english, language, speech, fairness, audit

Abstract: Automatic speech recognition (ASR) services are ubiquitous, transforming speech into text for systems like Amazon’s Alexa, Google’s Assistant, and Microsoft’s Cortana. Past research has identified discriminatory ASR performance as a function of racial group and nationality. In this paper, we expand the discussion about nationality and English language ASR by performing an audit of some of the most popular English ASR services using a large and global data set of speech from The Speech Accent Archive. 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. We discuss this bias in the context of the historical use of language to maintain global and political power.

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