Global Performance Disparities Between English-Language Accents in Automatic Speech Recognition
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Abstract (arXiv)
Past research has identified discriminatory automatic speech recognition (ASR) performance as a function of the racial group and nationality of the speaker. In this paper, we expand the discussion beyond bias as a function of the individual national origin of the speaker to look for bias as a function of the geopolitical orientation of their nation of origin. We audit some of the most popular English language ASR services 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 show that, even when controlling for multiple linguistic covariates, ASR service performance has a statistically significant relationship to the political alignment of the speaker's birth country with respect to the United States' geopolitical power. This holds for all ASR services tested. We discuss this bias in the context of the historical use of language to maintain global and political power.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 65 references that are in the map. First posted on arXiv 1 Aug 2022.
Builds on
9 of 65 references.
References · 9
- 2019Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learningcited by 116
- 2020Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligencecited by 100
- 2017Runaway Feedback Loops in Predictive Policingcited by 97
- 2022How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communicationscited by 30
- 2022Toward Fairness in Speech Recognition: Discovery and mitigation of performance disparitiescited by 27
- 2022Bias in Automated Speaker Recognitioncited by 20
- 2022Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Datacited by 15
- 2021Model-Based Approach for Measuring the Fairness in ASRcited by 13
- 2021Accented Speech Recognition: A Surveycited by 11
Built on by
Citing papers per year: 2024: 1; 2025: 2; 2026: 1.
4 papers.
- 2024The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Modelscited by 17
- 2025A Locally Executable AI System for Improving Preoperative Patient Communication: A Multi-Domain Clinical Evaluationcited by 0
- 2025Towards Fair ASR For Second Language Speakers Using Fairness Prompted Finetuningcited by 0
- 2026Unheard in the Digital Age: Rethinking AI Bias and Speech Diversitycited by 0
- 2026Unheard in the Digital Age: Rethinking AI Bias and Speech Diversity26 Jan 2026
- 2025Towards Fair ASR For Second Language Speakers Using Fairness Prompted Finetuning21 Oct 2025
- 2025A Locally Executable AI System for Improving Preoperative Patient Communication: A Multi-Domain Clinical Evaluation2 Oct 2025
- 2024The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Models12 Feb 2024