Automatic diagnosis and monitoring of Alzheimer�s disease can have a significant impact on society as well as the\nwell-being of patients. The part of the brain cortex that processes language abilities is one of the earliest parts to be\naffected by the disease. Therefore, detection of Alzheimer�s disease using speech-based features is gaining increasing\nattention. Here, we investigated an extensive set of features based on speech prosody as well as linguistic features\nderived from transcriptions of Turkish conversations with subjects with and without Alzheimer�s disease. Unlike most\nstandardized tests that focus on memory recall or structured conversations, spontaneous unstructured conversations\nare conducted with the subjects in informal settings. Age-, education-, and gender-controlled experiments are\nperformed to eliminate the effects of those three variables. Experimental results show that the proposed features\nextracted from the speech signal can be used to discriminate between the control group and the patients with\nAlzheimer�s disease. Prosodic features performed significantly better than the linguistic features. Classification\naccuracy over 80% was obtained with three of the prosodic features, but experiments with feature fusion did not\nfurther improve the classification performance.
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