# Global Performance Disparities Between English-Language Accents in Automatic Speech Recognition

arXiv: https://arxiv.org/abs/2208.01157 · HTML: https://mlsanity.com/paper/2208.01157/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 65 references that are in the map.
- First posted on arXiv 1 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)
- [Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence](https://mlsanity.com/paper/2007.04068/) (2020)
- [Runaway Feedback Loops in Predictive Policing](https://mlsanity.com/paper/1706.09847/) (2017)
- [How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications](https://mlsanity.com/paper/2203.16822/) (2022)
- [Toward Fairness in Speech Recognition: Discovery and mitigation of performance disparities](https://mlsanity.com/paper/2207.11345/) (2022)
- [Bias in Automated Speaker Recognition](https://mlsanity.com/paper/2201.09486/) (2022)
- [Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data](https://mlsanity.com/paper/2205.08014/) (2022)
- [Model-Based Approach for Measuring the Fairness in ASR](https://mlsanity.com/paper/2109.09061/) (2021)
- [Accented Speech Recognition: A Survey](https://mlsanity.com/paper/2104.10747/) (2021)

## Built on by (most cited first, 6 of 6)

- [The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Models](https://mlsanity.com/paper/2402.07658/) (2024)
- [A Locally Executable AI System for Improving Preoperative Patient Communication: A Multi-Domain Clinical Evaluation](https://mlsanity.com/paper/2510.01671/) (2025)
- [Towards Fair ASR For Second Language Speakers Using Fairness Prompted Finetuning](https://mlsanity.com/paper/2510.18374/) (2025)
- [Unheard in the Digital Age: Rethinking AI Bias and Speech Diversity](https://mlsanity.com/paper/2601.18641/) (2026)
- [A Sociolinguistic Analysis of Automatic Speech Recognition Bias in Newcastle English](https://mlsanity.com/paper/2603.24549/) (2026)
- [Mind the Accent Gap: British Accent Robustness in Speech-Driven Financial Voice Assistants](https://mlsanity.com/paper/2610.06587/) (2026)

## 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.