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

主讲人:钱辰

主讲人简介:2010年获得上海交通大学学士学位,2014年获得英国曼彻斯特大学硕士学位,2019年获得英国曼彻斯特大学博士学位。从事软件、人工智能等方面的教学和科研。

讲座摘要: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.

时间:1129日1230-13:00

地点:1号学院楼140