ISCA Archive SPSC 2021
ISCA Archive SPSC 2021

Racial Disparities in Automated Speech Recognition

Allison Koenecke

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 popular commercial ASR systems developed by Amazon, Apple, Google, IBM, and Microsoft. 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. See more at fairspeech.stanford.edu.


Cite as: Koenecke, A. (2021) Racial Disparities in Automated Speech Recognition. Proc. 2021 ISCA Symposium on Security and Privacy in Speech Communication,

@inproceedings{koenecke21_spsc,
  author={Allison Koenecke},
  title={{Racial Disparities in Automated Speech Recognition}},
  year=2021,
  booktitle={Proc. 2021 ISCA Symposium on Security and Privacy in Speech Communication},
  pages={}
}