Conference Paper/Proceeding/Abstract 330 views
Detecting Alzheimer’s Disease Using Interactional and Acoustic Features from Spontaneous Speech
Shamila Nasreen,
Julian Hough,
Matthew Purver
Interspeech 2021
Swansea University Author: Julian Hough
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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...
Published in: | Interspeech 2021 |
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ISBN: | 9781713836902 |
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2021
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URI: | https://cronfa.swan.ac.uk/Record/cronfa64933 |
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v2 64933 2023-11-07 Detecting Alzheimer&#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech 082d773ae261d2bbf49434dd2608ab40 Julian Hough Julian Hough true false 2023-11-07 SCS 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 Computer Science COLLEGE CODE SCS 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 2 Matthew Purver 3 |
title |
Detecting Alzheimer&#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech |
spellingShingle |
Detecting Alzheimer&#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech Julian Hough |
title_short |
Detecting Alzheimer&#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech |
title_full |
Detecting Alzheimer&#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech |
title_fullStr |
Detecting Alzheimer&#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech |
title_full_unstemmed |
Detecting Alzheimer&#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech |
title_sort |
Detecting Alzheimer&#8217;s Disease Using Interactional and Acoustic Features from Spontaneous Speech |
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082d773ae261d2bbf49434dd2608ab40 |
author_id_fullname_str_mv |
082d773ae261d2bbf49434dd2608ab40_***_Julian Hough |
author |
Julian Hough |
author2 |
Shamila Nasreen Julian Hough Matthew Purver |
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Conference Paper/Proceeding/Abstract |
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Interspeech 2021 |
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2021 |
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Swansea University |
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9781713836902 |
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10.21437/interspeech.2021-1526 |
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ISCA |
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Faculty of Science and Engineering |
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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 |
2021-08-30T14:12:27Z |
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11.031595 |