Performance Disparities Between Accents in Automatic Speech Recognition (Student Abstract)

Alex DiChristofano, Henry Shuster, Shefali Chandra, Neal Patwari

AAAI 2023 pp. 16200-16201

doi:10.1609/AAAI.V37I13.26960 /aaai/2023/dichristofano2023aaai-performance/

Abstract

In this work, we expand the discussion of bias in Automatic Speech Recognition (ASR) through a large-scale audit. Using a large and global data set of speech, we perform an audit of some of the most popular English ASR services. 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.

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Text

DiChristofano et al. "Performance Disparities Between Accents in Automatic Speech Recognition (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I13.26960

Markdown

[DiChristofano et al. "Performance Disparities Between Accents in Automatic Speech Recognition (Student Abstract)." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/dichristofano2023aaai-performance/) doi:10.1609/AAAI.V37I13.26960

BibTeX

@inproceedings{dichristofano2023aaai-performance,
  title     = {{Performance Disparities Between Accents in Automatic Speech Recognition (Student Abstract)}},
  author    = {DiChristofano, Alex and Shuster, Henry and Chandra, Shefali and Patwari, Neal},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2023},
  pages     = {16200-16201},
  doi       = {10.1609/AAAI.V37I13.26960},
  url       = {https://mlanthology.org/aaai/2023/dichristofano2023aaai-performance/}
}