3 citations · 4 across the 11 of their papers we have counts for
11 papers
Automated Detection and Classification of Delusion-related Content in Naturalistic Audio Diaries Using Multi-Agent Language Models
Feng Chen, Justin Tauscher, Changye Li +7
Speech monologues recorded in naturalistic settings provide opportunities to characterize mental illness phenomenology and detect symptom exacerbation. Large language models (LLMs)…
"Newspaper Eat" Means "Not Tasty": A Taxonomy and Benchmark for Coded Language in Real-World Chinese Online Reviews
Ruyuan Wan, Changye Li, Ting-Hao 'Kenneth' Huang
Coded language is an important part of human communication. It refers to cases where users intentionally encode meaning so that the surface text differs from the intended meaning a…
Mitigating Confounding in Speech-Based Dementia Detection through Weight Masking
Zhecheng Sheng, Xiruo Ding, Brian Hur +3
Deep transformer models have been used to detect linguistic anomalies in patient transcripts for early Alzheimer's disease (AD) screening. While pre-trained neural language models…
"Is There Anything Else?'': Examining Administrator Influence on Linguistic Features from the Cookie Theft Picture Description Cognitive Test
Changye Li, Zhecheng Sheng, Trevor Cohen +1
Alzheimer's Disease (AD) dementia is a progressive neurodegenerative disease that negatively impacts patients' cognitive ability. Previous studies have demonstrated that changes in…
Bigger But Not Better: Small Neural Language Models Outperform Large Language Models in Detection of Thought Disorder
Changye Li, Weizhe Xu, Serguei Pakhomov +3
Disorganized thinking is a key diagnostic indicator of schizophrenia-spectrum disorders. Recently, clinical estimates of the severity of disorganized thinking have been shown to co…
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…
Too Big to Fail: Larger Language Models are Disproportionately Resilient to Induction of Dementia-Related Linguistic Anomalies
Changye Li, Zhecheng Sheng, Trevor Cohen +1
As artificial neural networks grow in complexity, understanding their inner workings becomes increasingly challenging, which is particularly important in healthcare applications. T…
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'…
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…
The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia Classification
Changye Li, Trevor Cohen, Serguei Pakhomov
Linguistic anomalies detectable in spontaneous speech have shown promise for various clinical applications including screening for dementia and other forms of cognitive impairment.…
GPT-D: Inducing Dementia-related Linguistic Anomalies by Deliberate Degradation of Artificial Neural Language Models
Changye Li, David Knopman, Weizhe Xu +2
Deep learning (DL) techniques involving fine-tuning large numbers of model parameters have delivered impressive performance on the task of discriminating between language produced…