  As the aging of society continues to accelerate, Alzheimer's Disease (AD) has
received more and more attention from not only medical but also other fields,
such as computer science, over the past decade. Since speech is considered one
of the effective ways to diagnose cognitive decline, AD detection from speech
has emerged as a hot topic. Nevertheless, such approaches fail to tackle
several key issues: 1) AD is a complex neurocognitive disorder which means it
is inappropriate to conduct AD detection using utterance information alone
while ignoring dialogue information; 2) Utterances of AD patients contain many
disfluencies that affect speech recognition yet are helpful to diagnosis; 3) AD
patients tend to speak less, causing dialogue breakdown as the disease
progresses. This fact leads to a small number of utterances, which may cause
detection bias. Therefore, in this paper, we propose a novel AD detection
architecture consisting of two major modules: an ensemble AD detector and a
proactive listener. This architecture can be embedded in the dialogue system of
conversational robots for healthcare.
