Detection of Alzheimer's disease using prosodic cues in conversational speech

Automatic diagnosis of the Alzheimer's disease as well as monitoring of the diagnosed patients can make significant economic impact on societies. We investigated an automatic diagnosis approach through the use of speech based features. As opposed to standard tests that are mostly focused on memory recall, spontaneous conversations are carried with the subjects in informal settings. Prosodic speech features extracted from speech could discriminate between healthy people and the patients with high reliability. Although the patients were in later stages of Alzheimer's disease, results indicate the potential of speech-based automated solutions for Alzheimer's disease diagnosis. Moreover, the data collection process employed here can be done inexpensively by call center agents in a real-life application. Thus, the investigated techniques hold the potential to significantly reduce the financial burden on governments and Alzheimer' patients.