Unveiling Performance Bias in ASR Systems: A Study on Gender, Age, Accent, and More

Maliha Jahan, Priyam Mazumdar, Thomas Thebaud, Mark Hasegawa-Johnson, Jesús Villalba, Najim Dehak, Laureano Moro-Velazquez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the recent advancements in speech recognition, it is crucial to ensure these systems are free from performance biases against any speaker subgroups. This study examined the performance of twenty variants of seven Automatic Speech Recognition models across four datasets in English language: L2 Arctic, Speech Accent Archive, CORAAL, and SBCSAE. We employed Poisson regression and drop-in-deviance tests to identify which attributes significantly contribute to the Word Error Rate. Our analysis revealed biases related to attributes such as native language, location, occupation, and birthplace. Most systems did not exhibit bias related to factors like gender and age. Additionally, we conducted an experiment to detect bias related to”variant” (accent and dialect) by combining the CORAAL (African American Vernacular English (AAVE)) and SBCSAE (General American English (GAE)) datasets, aiming to identify the sources of any observed bias. We found that both speaker variability and dialectal difference contribute to observed bias for variant.

Original languageEnglish (US)
Title of host publication2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Proceedings
EditorsBhaskar D Rao, Isabel Trancoso, Gaurav Sharma, Neelesh B. Mehta
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350368741
DOIs
StatePublished - 2025
Event2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025 - Hyderabad, India
Duration: Apr 6 2025Apr 11 2025

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025
Country/TerritoryIndia
CityHyderabad
Period4/6/254/11/25

Keywords

  • automatic speech recognition
  • dataset analysis
  • drop-in-deviance test
  • fairness analysis
  • Poisson regression
  • speaker variability
  • speech recognition bias

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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