Detecting Mild Cognitive Impairment in Alzheimer’s Disease using Speech Acoustics Only: A Two-Stage Deep Metric Learning Approach

Recent studies have shown that spontaneous speech can be exploited for cognitive decline screening in individuals with Alzheimer’s dementia. However, as the intermediate state between healthy control (HC) and Alzheimer’s disease (AD), mild cognitive impairment (MCI) is challenging to be distinguished from the other two. In order to tackle the problem, this paper proposes a two-stage metric learning approach. Each stage takes distinct acoustic features as input to a specific deep neural network. Moreover, we present an online triplet generator that can maximize sample utilization efficiency by investigating the decorrelation among samples. Finally, the experimental results prove that our proposed approach can significantly improve the accuracy of MCI detection and hence perform an excellent classification between subjects with AD, HC, and MCI.