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

Authors

  • Alex DiChristofano Division of Computational & Data Sciences, Washington University in St. Louis
  • Henry Shuster Department of Computer Science & Engineering, Washington University in St. Louis
  • Shefali Chandra Department of Women, Gender, and Sexuality Studies, Washington University in St. Louis Department of History, Washington University in St. Louis
  • Neal Patwari Division of Computational & Data Sciences, Washington University in St. Louis Department of Computer Science & Engineering, Washington University in St. Louis Department of Electrical & Systems Engineering, Washington University in St. Louis

DOI:

https://doi.org/10.1609/aaai.v37i13.26960

Keywords:

Bias, Automatic Speech Recognition, Accent, Fairness, Dialect, Natural Language Processing, Audit, Speech, English, Language, Artificial Intelligence, Machine Learning

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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Published

2024-07-15

How to Cite

DiChristofano, A., Shuster, H., Chandra, S., & Patwari, N. (2024). Performance Disparities between Accents in Automatic Speech Recognition (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16200-16201. https://doi.org/10.1609/aaai.v37i13.26960

Issue

Vol. 37 No. 13: AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program