The efficacy of memory load on speech-based detection of Alzheimer's disease.

ABSTRACT

Introduction: The study aims to test whether an increase in memory load could improve the efficacy in detection of Alzheimer's disease and prediction of the Mini-Mental State Examination (MMSE) score. Methods: Speech from 45 mild-to-moderate Alzheimer's disease patients and 44 healthy older adults were collected using three speech tasks with varying memory loads. We investigated and compared speech characteristics of Alzheimer's disease across speech tasks to examine the effect of memory load on speech characteristics. Finally, we built Alzheimer's disease classification models and MMSE prediction models to assess the diagnostic value of speech tasks. Results: The speech characteristics of Alzheimer's disease in pitch, loudness, and speech rate were observed and the high-memory-load task intensified such characteristics. The high-memory-load task outperformed in AD classification with an accuracy of 81.4% and MMSE prediction with a mean absolute error of 4.62. Discussion: The high-memory-load recall task is an effective method for speech-based Alzheimer's disease detection.