The worldwide population is aging. With a larger population of elderly
people, the numbers of people affected by cognitive impairment such
as Alzheimer’s disease are growing. Unfortunately, there is no
known cure for Alzheimer’s disease. The only way to alleviate
it’s serious effects is to start therapy very early before the
disease has wrought too much irreversible damage. Current diagnostic
procedures are neither cost nor time efficient and therefore do not
meet the demands for frequent mass screening required to mitigate the
consequences of cognitive impairments on the global scale.
We present an experiment
to detect Alzheimer’s disease using spontaneous conversational
speech. The speech data was recorded during biographic interviews in
the Interdisciplinary Longitudinal Study on Adult Development and Aging
(ILSE), a large data resource on healthy and satisfying aging in middle
adulthood and later life in Germany. From these recordings we extract
ten speech-based features using voice activity detection and transcriptions.
In an experimental setup with 98 data samples we train a linear discriminant
analysis classifier to distinguish subjects with Alzheimer’s
disease from the control group. This setup results in an F-score of
0.8 for the detection of Alzheimer’s disease, clearly showing
our approach detects dementia well.