Eli Kurtz, Youxiang Zhu, Tiffany Driesse, Bang Tran, John A. Batsis, Robert M. Roth, and Xiaohui Liang
Early detection of Alzheimer’s Disease and Related Dementias (ADRD) is critical in treating the progression of the disease. Previous studies have shown that ADRD can be detected and classified using machine learning models trained on samples of spontaneous speech. We propose using Voice-Assistant Systems (VAS), e.g., Amazon Alexa, to monitor and
collect data from at-risk adults, and we show that this data can be used to achieve functional accuracy in classifying their cognitive status. In this paper, we develop multiple unique feature sets from VAS data that can be used in the training of machine learning models. We then perform multi-class classification, binary classification, and regression using these features on our dataset of older adults with three varying stages of cognitive decline interacting with VAS. Our results show that the VAS data can be used to classify Dementia (DM), Mild Cognitive Impairment (MCI), and Healthy Control (HC) participants with an accuracy up to 74.7%, and classify between HC and MCI with accuracy up to 62.8%.
Accepted by IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), 2023