Title:
Racial disparities in automated speech recognition
Authors:
Allison Koenecke, Andrew Nam, Emily Lake, Joe Nudell, Minnie Quartey, Zion Mengesha, Connor Toups, John R. Rickford, Dan Jurafsky & Sharad Goel
Published:
PNAS (Proceedings of the National Academy of Sciences of the United States of America), 117(14): 7684-7689; first published March 23, 2020.
Available: https://doi.org/10.1073/pnas.1915768117
Significance:
"Automated speech recognition (ASR) systems are now used in a variety of applications to convert spoken language to text, from virtual assistants, to closed captioning, to hands-free computing. By analyzing a large corpus of sociolinguistic interviews with white and African American speakers, we demonstrate large racial disparities in the performance of five popular commercial ASR systems. Our results point to hurdles faced by African Americans in using increasingly widespread tools driven by speech recognition technology. More generally, our work illustrates the need to audit emerging machine-learning systems to ensure they are broadly inclusive."