  Cognitive decline is a sign of Alzheimer's disease (AD), and there is
evidence that tracking a person's eye movement, using eye tracking devices, can
be used for the automatic identification of early signs of cognitive decline.
However, such devices are expensive and may not be easy-to-use for people with
cognitive problems. In this paper, we present a new way of capturing similar
visual features, by using the speech of people describing the Cookie Theft
picture - a common cognitive testing task - to identify regions in the picture
prompt that will have caught the speaker's attention and elicited their speech.
After aligning the automatically recognised words with different regions of the
picture prompt, we extract information inspired by eye tracking metrics such as
coordinates of the area of interests (AOI)s, time spent in AOI, time to reach
the AOI, and the number of AOI visits. Using the DementiaBank dataset we train
a binary classifier (AD vs. healthy control) using 10-fold cross-validation and
achieve an 80% F1-score using the timing information from the forced alignments
of the automatic speech recogniser (ASR); this achieved around 72% using the
timing information from the ASR outputs.
