Our paper on “More Data, Better Accuracy? An Empirical Study on Few-shot Speech-based Dementia Detection” was accepted by the Southern California Natural Language Processing Symposium (SoCal), 2022.
Dr. Liang delivered a keynote speech on “Exploiting Human Speech for Early Detection of Cognitive Decline” at IEEE International Conference on Universal Village on Oct. 24, 2022.
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”
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￼”
Our poster “Older Adults experience and response to Voice Assistant Systems” was chosen as the best poster in the category of Geriatric Syndromes (descriptive research on the mechanisms, natural history or management of major geriatric syndromes) at the American Geriatrics Society (AGS) 2022.
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”
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”
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”
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″
Check here for a list of Dementia Detection Papers with Keywords https://github.com/billzyx/awesome-dementia-detection The list is maintained by Youxiang Zhu.