Speech-Based Detection of Alzheimer's Disease in Conversational German

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.