2015
DOI: 10.1186/s13636-015-0052-y
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Evaluation of linguistic and prosodic features for detection of Alzheimer’s disease in Turkish conversational speech

Abstract: 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 conv… Show more

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Cited by 63 publications
(53 citation statements)
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“…There is extensive literature covering the analysis of the linguistic characteristics of AD patients [6,7,24,[28][29][30][31][32][33][34]. As part of our evaluation, we selected those that most authors have found to correlate significantly with the disease and that could be used in picture description tasks (Table 1).…”
Section: Linguistic Characteristicsmentioning
confidence: 99%
“…There is extensive literature covering the analysis of the linguistic characteristics of AD patients [6,7,24,[28][29][30][31][32][33][34]. As part of our evaluation, we selected those that most authors have found to correlate significantly with the disease and that could be used in picture description tasks (Table 1).…”
Section: Linguistic Characteristicsmentioning
confidence: 99%
“…From the study, they found that patients often not only uttered incomplete phrases but also interrupted others. These degraded the intelligibility of their speech [15].…”
Section: Related Workmentioning
confidence: 99%
“…After the utterance-based classification is performed, we make the final verdict for each subject based on the proportion of each class; we classified a subject into AD if he/she has AD percentage above the half. The utterance-level subject classification is expected to give more detailed information about the symptoms while most of the previous studies conducted the subject-level classification [15,3,12,13,9,16].…”
Section: Framework Overviewmentioning
confidence: 99%
“…Automatic speech processing has been shown to be a promising way forward in the diagnosis of dementia. Approaches have used acoustic or prosodic features [3,4,5,10,11] and linguistic or text-based features [12,6,7,10,13,14,15,16,17] in a classification task that aims to distinguish speakers affected by dementia from cognitively health speakers using just their speech.…”
Section: Introductionmentioning
confidence: 99%
“…Nonetheless automatic transcripts of elderly speech have successfully been used to detect dementia in standardized texts [12,7,15] although the word error rates for individual subjects reached over 90% [15]. Still, many of the approaches that have used text based features relied on manual transcriptions [10,13]. If we find dementia detection using features extracted from automatic transcriptions to work as well as dementia detection using features extracted from manual transcripts, then we no longer need to rely on manual transcriptions.…”
Section: Introductionmentioning
confidence: 99%