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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Abstract	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.
AbstractList	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 (G1, G2, and G3) based on the results of their Mini-mental state examination (MMSE): G1:95 normal (NOR) individuals with an MMSE greater than 26; G2:65 MCI subjects with 20 ≤ MMSE ≤ 26; G3:40 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. 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.
Author	Pantzaris, Marios Loizou, Christos P.
Author_xml	– sequence: 1 givenname: Christos P. orcidid: 0000-0003-1247-8573 surname: Loizou fullname: Loizou, Christos P. email: christos.loizou@cut.ac.cy organization: Faculty of Engineering & Technology, Department of Electrical Engineering and Computer Engineering and Informatics, Cyprus University of Technology – sequence: 2 givenname: Marios surname: Pantzaris fullname: Pantzaris, Marios organization: Cyprus Institute of Neurology and Genetics
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Snippet	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...
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SubjectTerms	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
Title	An automated speech analysis system for the detection of cognitive decline in elderly
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