Decoding disparities: evaluating automatic speech recognition system performance in transcribing Black and White patient verbal communication with nurses in home healthcare.
As artificial intelligence evolves, integrating speech processing into home healthcare (HHC) workflows is increasingly feasible. Audio-recorded communications enhance risk identification models, with automatic speech recognition (ASR) systems as a key component. This study evaluates the transcription accuracy and equity of 4 ASR systems-Amazon Web Services (AWS) General, AWS Medical, Whisper, and Wave2Vec-in transcribing patient-nurse communication in US HHC, focusing on their ability in accurate transcription of speech from Black and [...]
Author(s): Zolnoori, Maryam, Vergez, Sasha, Xu, Zidu, Esmaeili, Elyas, Zolnour, Ali, Anne Briggs, Krystal, Scroggins, Jihye Kim, Hosseini Ebrahimabad, Seyed Farid, Noble, James M, Topaz, Maxim, Bakken, Suzanne, Bowles, Kathryn H, Spens, Ian, Onorato, Nicole, Sridharan, Sridevi, McDonald, Margaret V
DOI: 10.1093/jamiaopen/ooae130