Exploiting Longitudinal Speech Sessions via Voice Assistant Systems for Early Detection of Cognitive Decline
Kristin Qi, Jiatong Shi, Caroline Summerour, John A. Batsis, Xiaohui Liang
TL;DR
A longitudinal study using voice assistant systems (VAS) to remotely collect seven-session speech data at three-month intervals across 18 months confirms the potential of VAS-based speech sessions for early detection of cognitive decline.
Abstract
Mild Cognitive Impairment (MCI) is an early stage of Alzheimer's disease (AD), a form of neurodegenerative disorder. Early identification of MCI is crucial for delaying its progression through timely interventions. Existing research has demonstrated the feasibility of detecting MCI using speech collected from clinical interviews or digital devices. However, these approaches typically analyze data collected at limited time points, limiting their ability to identify cognitive changes over time. This paper presents a longitudinal study using voice assistant systems (VAS) to remotely collect seven-session speech data at three-month intervals across 18 months. We propose two methods to improve MCI detection and the prediction of cognitive changes. The first method incorporates historical data, while the second predicts cognitive changes at two time points. Our results indicate improvements when incorporating historical data: the average F1-score for MCI detection improves from 58.6% to 71.2% (by 12.6%) in the case of acoustic features and from 62.1% to 75.1% (by 13.0%) in the case of linguistic features. Additionally, the prediction of cognitive changes achieves an F1-score of 73.7% in the case of acoustic features. These results confirm the potential of VAS-based speech sessions for early detection of cognitive decline.