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Conference Paper/Proceeding/Abstract 346 views

Detecting Alzheimer’s Disease Using Interactional and Acoustic Features from Spontaneous Speech

Shamila Nasreen, Julian Hough Orcid Logo, Matthew Purver

Interspeech 2021

Swansea University Author: Julian Hough Orcid Logo

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DOI (Published version): 10.21437/interspeech.2021-1526

Abstract

Alzheimer’s Disease (AD) is a form of Dementia that manifests in cognitive decline including memory, language, and changes in behavior. Speech data has proven valuable for inferring cognitive status, used in many health assessment tasks, and can be easily elicited in natural settings. Much work focu...

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Published in: Interspeech 2021
ISBN: 9781713836902
Published: ISCA ISCA 2021
URI: https://cronfa.swan.ac.uk/Record/cronfa64933
first_indexed 2023-11-07T22:31:15Z
last_indexed 2024-11-25T14:15:02Z
id cronfa64933
recordtype SURis
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spelling 2024-03-12T14:12:30.6901473 v2 64933 2023-11-07 Detecting Alzheimer&amp;#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech 082d773ae261d2bbf49434dd2608ab40 0000-0002-4345-6759 Julian Hough Julian Hough true false 2023-11-07 MACS Alzheimer’s Disease (AD) is a form of Dementia that manifests in cognitive decline including memory, language, and changes in behavior. Speech data has proven valuable for inferring cognitive status, used in many health assessment tasks, and can be easily elicited in natural settings. Much work focuses on analysis using linguistic features; here, we focus on non-linguistic features and their use in distinguishing AD patients from similar-age Non-AD patients with other health conditions in the Carolinas Conversation Collection (CCC) dataset. We used two types of features: patterns of interaction including pausing behaviour and floor control, and acoustic features including pitch, amplitude, energy, and cepstral coefficients. Fusion of the two kinds of features, combined with feature selection, obtains very promising classification results: classification accuracy of 90% using standard models such as support vector machines and logistic regression. We also obtain promising results using interactional features alone (87% accuracy), which can be easily extracted from natural conversations in daily life and thus have the potential for future implementation as a noninvasive method for AD diagnosis and monitoring. Conference Paper/Proceeding/Abstract Interspeech 2021 ISCA ISCA 9781713836902 30 8 2021 2021-08-30 10.21437/interspeech.2021-1526 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Not Required 2024-03-12T14:12:30.6901473 2023-11-07T22:07:40.7390587 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Shamila Nasreen 1 Julian Hough 0000-0002-4345-6759 2 Matthew Purver 3
title Detecting Alzheimer&amp;#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech
spellingShingle Detecting Alzheimer&amp;#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech
Julian Hough
title_short Detecting Alzheimer&amp;#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech
title_full Detecting Alzheimer&amp;#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech
title_fullStr Detecting Alzheimer&amp;#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech
title_full_unstemmed Detecting Alzheimer&amp;#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech
title_sort Detecting Alzheimer&amp;#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech
author_id_str_mv 082d773ae261d2bbf49434dd2608ab40
author_id_fullname_str_mv 082d773ae261d2bbf49434dd2608ab40_***_Julian Hough
author Julian Hough
author2 Shamila Nasreen
Julian Hough
Matthew Purver
format Conference Paper/Proceeding/Abstract
container_title Interspeech 2021
publishDate 2021
institution Swansea University
isbn 9781713836902
doi_str_mv 10.21437/interspeech.2021-1526
publisher ISCA
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 0
active_str 0
description Alzheimer’s Disease (AD) is a form of Dementia that manifests in cognitive decline including memory, language, and changes in behavior. Speech data has proven valuable for inferring cognitive status, used in many health assessment tasks, and can be easily elicited in natural settings. Much work focuses on analysis using linguistic features; here, we focus on non-linguistic features and their use in distinguishing AD patients from similar-age Non-AD patients with other health conditions in the Carolinas Conversation Collection (CCC) dataset. We used two types of features: patterns of interaction including pausing behaviour and floor control, and acoustic features including pitch, amplitude, energy, and cepstral coefficients. Fusion of the two kinds of features, combined with feature selection, obtains very promising classification results: classification accuracy of 90% using standard models such as support vector machines and logistic regression. We also obtain promising results using interactional features alone (87% accuracy), which can be easily extracted from natural conversations in daily life and thus have the potential for future implementation as a noninvasive method for AD diagnosis and monitoring.
published_date 2021-08-30T20:26:21Z
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