An automated speech analysis system for the detection of cognitive decline in elderly

The goal of this study is to develop and test an automated integrated speech analysis system for detecting mild cognitive impairment (MCI) and dementia in spontaneous free speech. During the years 2010–2016, speech recordings (N = 2800) were obtained from 200 Greek Cypriots over the age of 65. These...

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Vydáno v:International journal of speech technology Ročník 26; číslo 2; s. 337 - 353
Hlavní autoři: Loizou, Christos P., Pantzaris, Marios
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.07.2023
Springer Nature B.V
Témata:
Artificial Intelligence
Automation
Classification
Classifiers
Cognition
Cognitive impairment
Computer generated language analysis
Dementia
Engineering
Freedom of speech
Groups
Mini-Mental State Examination
Mobile phones
Older people
Signal,Image and Speech Processing
Social Sciences
Speech recognition
Spontaneous speech
Statistics
Cognitive decline
Glottal features
Classification analysis
Mild cognitive impairment
Speech signal analysis
Alzheimer’s
Dementia
ISSN:1381-2416, 1572-8110
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Popis
Shrnutí:The goal of this study is to develop and test an automated integrated speech analysis system for detecting mild cognitive impairment (MCI) and dementia in spontaneous free speech. During the years 2010–2016, speech recordings (N = 2800) were obtained from 200 Greek Cypriots over the age of 65. These were divided into three groups (G 1 , G 2 , and G 3 ) based on the results of their Mini-mental state examination (MMSE): G 1 :95 normal (NOR) individuals with an MMSE greater than 26; G 2 :65 MCI subjects with 20 ≤ MMSE ≤ 26; G 3 :40 dementia subjects with 0 ≤ MMSE < 20. As a result, each speech recording was analyzed for 55 different speech features. The features that could statistically significantly distinguish between the three aforementioned groups were selected using statistical and model multi-classification analysis. Learning-based classifiers were built using the selected features alone or in combination. For each group, statistically significant differences in speech features were detected, which may be used to differentiate the three groups. An overall multi-classification area under the curve (AUC) of 0.92 was attained using only the features identified plus clinical factors. Speech features were extracted, and they were able to discriminate people from the three groups. This study paves the way for the development of an integrated system that uses automatic speech analysis to detect early and progressive signs of cognitive decline (CD) in free speech. In a future study, the proposed method will be developed and integrated into a mobile device.
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ISSN:1381-2416
1572-8110
DOI:10.1007/s10772-023-10016-1