(Related) Should Alexa diagnose Alzheimer’s?: Legal and ethical issues with at-home consumer devices

Voice-based AI-powered digital assistants, such as Alexa, Siri, and Google Assistant, present an exciting opportunity to translate healthcare from the hospital to the home. But building a digital, medical panopticon can raise many legal and ethical challenges if not designed and implemented thoughtfully. This paper highlights the benefits and explores some of the challenges ofContinue reading “(Related) Should Alexa diagnose Alzheimer’s?: Legal and ethical issues with at-home consumer devices”

One paper accepted by INTERSPEECH 2022

Towards Interpretability of Speech Pause in Dementia Detection using Adversarial Learning, Youxiang Zhu, Xiaohui Liang, John A. Batsis (University of North Carolina), Robert M. Roth (Dartmouth) Abstract: Detecting dementia using human speech is promising but faces a limited data challenge. While recent research has shown general pretrained models (e.g., BERT) can be applied to improve dementia detection,Continue reading “One paper accepted by INTERSPEECH 2022”

Phase II update

We have successfully recruited 32 older adults for our Phase II evaluation. 13 participants (5 healthy, 8 mild cognitive impairment) chose Pathway 1, i.e., performing seven sessions once every three months + home data tracking 19 participants (10 healthy, 9 mild cognitive impairment) chose Pathway 2, i.e., performing seven sessions only once every three monthsContinue reading “Phase II update”

Patient Perceptions of Using Voice-Based Dietary Assessment Tools Among Older Adults

Tiffany M. Driesse, Xiaohui Liang, Michael Fowler, Jing Yuan, and John A. Batsis Background: Poor diet among older adults is a risk factor for developing multiple chronic diseases. Dietary recall comprises an important component in intervention research and clinical care. Commonly used tools include the web-based automated self-administered 24-hour assessment (ASA-24). Yet voice assistant (VAS)Continue reading “Patient Perceptions of Using Voice-Based Dietary Assessment Tools Among Older Adults”

Domain-aware Intermediate Pretraining for Dementia Detection with Limited Data

Youxiang Zhu, Xiaohui Liang, John A. Batsis, and Robert M. Roth Detecting dementia using human speech is promising but faces a limited data challenge. While recent research has shown general pretrained models (e.g., BERT) can be applied to improve dementia detection, the pretrained model can hardly be fine-tuned with the available small dementia dataset asContinue reading “Domain-aware Intermediate Pretraining for Dementia Detection with Limited Data”

Two papers accepted by “IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)” 2022

Our two papers have been accepted by IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP) 2022 Towards Interpretability of Speech Pause in Dementia Detection using Adversarial Learning, Youxiang Zhu, Bang Tran, Xiaohui Liang, John A. Batsis (University of North Carolina), Robert M. Roth (Dartmouth) Speech Tasks Relevant to Sleepiness Determined with Deep Transfer Learning, BangContinue reading “Two papers accepted by “IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP)” 2022″