{"id":{"repo_id":"trento","oai_identifier":"oai:iris.unitn.it:11572/389289"},"canonical_url":"https://search.dev.ndltd.org/etd/trento/oai:iris.unitn.it:11572/389289","repository":{"repo_id":"trento","name":"Università degli Studi di Trento","base_url":"https://iris.unitn.it/oai/request"},"display":{"title":"SPONTANEOUS SPEECH ANALYSIS FOR DETECTING MILD COGNITIVE IMPAIRMENT AND ALZHEIMER’S DISEASE IN THAI OLDER ADULTS","abstract":"Memory deficits in Alzheimer’s disease (AD) and mild cognitive impairment (MCI) can be reflected in language-based tests, especially spontaneous speech tasks. Three spontaneous speech tests were developed in this study, including Thai Picture description (TPD), Thai Story Recall (TSR), and Semi-structured Interview for Thai (SIT) Ninety-eight Thai older adults underwent screening tests and three spontaneous speech tests. Then they were classified into three groups, including healthy control (HC), MCI, and AD. Their verbal responses were extracted into the content variables and acoustic features. Then the discriminant ability and accuracy in differentiating HC, MCI, and AD were examined with by Multivariate Discriminant Analysis (MDA) and analysis of the ROC curve and AUC. Two content variables showed significant differences among three groups of participants, i.e., correct information unit (CIU) of the TPD and delayed recall scores of the TSR. For acoustic features, ANOVAs revealed that three variables were significantly different among the three experimental groups, i.e., total utterance time in delayed recall, number of voice breaks in the TPD, and the SIT. The result of a stepwise estimation in MDA presented that the best combination of predictive model was CIU and backward digit span (BDS), in which provides 61.1% of correct classification. This discriminant function showed AUC of .81 in differentiating HC and MCI, AUC of .91 in distinguishing HC and AD, and AUC of .86 in detecting persons with cognitive impairments (MCI and AD) from HC. In conclusion, the combination of CIU of TPD and BDS is suitable for differentiating AD and persons with cognitive impairments from HC. However, there is no appropriate predictor in distinguishing MCI and AD.","abstract_html":"Memory deficits in Alzheimer’s disease (AD) and mild cognitive impairment (MCI) can be reflected in language-based tests, especially spontaneous speech tasks. Three spontaneous speech tests were developed in this study, including Thai Picture description (TPD), Thai Story Recall (TSR), and Semi-structured Interview for Thai (SIT) Ninety-eight Thai older adults underwent screening tests and three spontaneous speech tests. Then they were classified into three groups, including healthy control (HC), MCI, and AD. Their verbal responses were extracted into the content variables and acoustic features. Then the discriminant ability and accuracy in differentiating HC, MCI, and AD were examined with by Multivariate Discriminant Analysis (MDA) and analysis of the ROC curve and AUC. Two content variables showed significant differences among three groups of participants, i.e., correct information unit (CIU) of the TPD and delayed recall scores of the TSR. For acoustic features, ANOVAs revealed that three variables were significantly different among the three experimental groups, i.e., total utterance time in delayed recall, number of voice breaks in the TPD, and the SIT. The result of a stepwise estimation in MDA presented that the best combination of predictive model was CIU and backward digit span (BDS), in which provides 61.1% of correct classification. This discriminant function showed AUC of .81 in differentiating HC and MCI, AUC of .91 in distinguishing HC and AD, and AUC of .86 in detecting persons with cognitive impairments (MCI and AD) from HC. In conclusion, the combination of CIU of TPD and BDS is suitable for differentiating AD and persons with cognitive impairments from HC. However, there is no appropriate predictor in distinguishing MCI and AD.","abstract_has_math":false,"creators":["Na Chiangmai, Natinee"],"institution":"Università degli studi di Trento","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Wongupparaj, Peera","Job, Remo","Mulatti, Claudio"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-10-17","date_published":"2023-10-17","updated_at":"2026-07-24T05:04:35Z","subjects":["Neurocognitive assessment, Language-based tasks, Spontaneous speech analysis, Cognitive deficits, Dementia"],"languages":["eng"],"rights":["info:eu-repo/semantics/openAccess","license:Creative commons","license uri:http://creativecommons.org/licenses/by-nc-nd/4.0/"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["http://dx.doi.org/10.15168/11572_389289","10.15168/11572_389289"],"render_values":[{"text":"http://dx.doi.org/10.15168/11572_389289","href":"http://dx.doi.org/10.15168/11572_389289","code":true},{"text":"10.15168/11572_389289","href":"https://doi.org/10.15168/11572_389289","code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/11572/389289","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wongupparaj, Peera","Na Chiangmai, Natinee","Job, Remo","Mulatti, Claudio"]},{"key":"dc:creator","label":"Author","values":["Na Chiangmai, Natinee"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-10-17"]},{"key":"dc:publisher","label":"Institution","values":["Università degli studi di Trento","place:TRENTO"]},{"key":"dc:relation","label":"Dc Relation","values":["firstpage:1","lastpage:236","numberofpages:236","alleditors:Wongupparaj, Peera"]},{"key":"dc:type","label":"Dc Type","values":["info:eu-repo/semantics/doctoralThesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Neurocognitive assessment, Language-based tasks, Spontaneous speech analysis, Cognitive deficits, Dementia"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["info:eu-repo/semantics/openAccess","license:Creative commons","license uri:http://creativecommons.org/licenses/by-nc-nd/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/11572/389289","http://dx.doi.org/10.15168/11572_389289","10.15168/11572_389289"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Memory deficits in Alzheimer’s disease (AD) and mild cognitive impairment (MCI) can be reflected in language-based tests, especially spontaneous speech tasks. Three spontaneous speech tests were developed in this study, including Thai Picture description (TPD), Thai Story Recall (TSR), and Semi-structured Interview for Thai (SIT) Ninety-eight Thai older adults underwent screening tests and three spontaneous speech tests. Then they were classified into three groups, including healthy control (HC), MCI, and AD. Their verbal responses were extracted into the content variables and acoustic features. Then the discriminant ability and accuracy in differentiating HC, MCI, and AD were examined with by Multivariate Discriminant Analysis (MDA) and analysis of the ROC curve and AUC. Two content variables showed significant differences among three groups of participants, i.e., correct information unit (CIU) of the TPD and delayed recall scores of the TSR. For acoustic features, ANOVAs revealed that three variables were significantly different among the three experimental groups, i.e., total utterance time in delayed recall, number of voice breaks in the TPD, and the SIT. The result of a stepwise estimation in MDA presented that the best combination of predictive model was CIU and backward digit span (BDS), in which provides 61.1% of correct classification. This discriminant function showed AUC of .81 in differentiating HC and MCI, AUC of .91 in distinguishing HC and AD, and AUC of .86 in detecting persons with cognitive impairments (MCI and AD) from HC. In conclusion, the combination of CIU of TPD and BDS is suitable for differentiating AD and persons with cognitive impairments from HC. However, there is no appropriate predictor in distinguishing MCI and AD."]},{"key":"dc:title","label":"Title","values":["SPONTANEOUS SPEECH ANALYSIS FOR DETECTING MILD COGNITIVE IMPAIRMENT AND ALZHEIMER’S DISEASE IN THAI OLDER ADULTS"]}]}],"canonical_facts":{"dc:contributor":["Wongupparaj, Peera","Na Chiangmai, Natinee","Job, Remo","Mulatti, Claudio"],"dc:creator":["Na Chiangmai, Natinee"],"dc:date":["2023-10-17"],"dc:description":["Memory deficits in Alzheimer’s disease (AD) and mild cognitive impairment (MCI) can be reflected in language-based tests, especially spontaneous speech tasks. Three spontaneous speech tests were developed in this study, including Thai Picture description (TPD), Thai Story Recall (TSR), and Semi-structured Interview for Thai (SIT) Ninety-eight Thai older adults underwent screening tests and three spontaneous speech tests. Then they were classified into three groups, including healthy control (HC), MCI, and AD. Their verbal responses were extracted into the content variables and acoustic features. Then the discriminant ability and accuracy in differentiating HC, MCI, and AD were examined with by Multivariate Discriminant Analysis (MDA) and analysis of the ROC curve and AUC. Two content variables showed significant differences among three groups of participants, i.e., correct information unit (CIU) of the TPD and delayed recall scores of the TSR. For acoustic features, ANOVAs revealed that three variables were significantly different among the three experimental groups, i.e., total utterance time in delayed recall, number of voice breaks in the TPD, and the SIT. The result of a stepwise estimation in MDA presented that the best combination of predictive model was CIU and backward digit span (BDS), in which provides 61.1% of correct classification. This discriminant function showed AUC of .81 in differentiating HC and MCI, AUC of .91 in distinguishing HC and AD, and AUC of .86 in detecting persons with cognitive impairments (MCI and AD) from HC. In conclusion, the combination of CIU of TPD and BDS is suitable for differentiating AD and persons with cognitive impairments from HC. 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