3 citations · 4 across the 5 of their papers we have counts for
5 papers
Reexamining Racial Disparities in Automatic Speech Recognition Performance: The Role of Confounding by Provenance
Changye Li, Trevor Cohen, Serguei Pakhomov
Automatic speech recognition (ASR) models trained on large amounts of audio data are now widely used to convert speech to written text in a variety of applications from video capti…
Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?
Changye Li, Weizhe Xu, Trevor Cohen +1
\textbf{Objectives}: We aimed to investigate how errors from automatic speech recognition (ASR) systems affect dementia classification accuracy, specifically in the ``Cookie Theft'…
Backdoor Adjustment of Confounding by Provenance for Robust Text Classification of Multi-institutional Clinical Notes
Xiruo Ding, Zhecheng Sheng, Meliha Yetişgen +2
Natural Language Processing (NLP) methods have been broadly applied to clinical tasks. Machine learning and deep learning approaches have been used to improve the performance of cl…
A Dialogue System for Assessing Activities of Daily Living: Improving Consistency with Grounded Knowledge
Zhecheng Sheng, Raymond Finzel, Michael Lucke +3
In healthcare, the ability to care for oneself is reflected in the "Activities of Daily Living (ADL)," which serve as a measure of functional ability (functioning). A lack of funct…
TRESTLE: Toolkit for Reproducible Execution of Speech, Text and Language Experiments
Changye Li, Weizhe Xu, Trevor Cohen +2
The evidence is growing that machine and deep learning methods can learn the subtle differences between the language produced by people with various forms of cognitive impairment s…