Khodabakhsh, AliYesil, FatihGuner, EkremDemiroğlu, Cenk2015-12-282015-12-282015-121687-4722http://hdl.handle.net/10679/1348https://doi.org/10.1186/s13636-015-0052-yDue to copyright restrictions, the access to the full text of this article is only available via subscription.Automatic diagnosis and monitoring of Alzheimer’s disease can have a significant impact on society as well as the well-being of patients. The part of the brain cortex that processes language abilities is one of the earliest parts to be affected by the disease. Therefore, detection of Alzheimer’s disease using speech-based features is gaining increasing attention. Here, we investigated an extensive set of features based on speech prosody as well as linguistic features derived from transcriptions of Turkish conversations with subjects with and without Alzheimer’s disease. Unlike most standardized tests that focus on memory recall or structured conversations, spontaneous unstructured conversations are conducted with the subjects in informal settings. Age-, education-, and gender-controlled experiments are performed to eliminate the effects of those three variables. Experimental results show that the proposed features extracted from the speech signal can be used to discriminate between the control group and the patients with Alzheimer’s disease. Prosodic features performed significantly better than the linguistic features. Classification accuracy over 80% was obtained with three of the prosodic features, but experiments with feature fusion did not further improve the classification performance.enginfo:eu-repo/semantics/openAccessAttribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/Article00035264650000110.1186/s13636-015-0052-yAlzheimer’s diseaseSpeech processingLinguistic featuresProsodic featuresMachine learning2-s2.0-84927153783