Learning Language and Acoustic Models for Identifying Alzheimer’s Dementia From Speech

Alzheimer’s dementia (AD) is a chronic neurodegenerative illness that manifests in a gradual decline of cognitive function. Early identification of AD is essential for managing the ensuing cognitive deficits, which may lead to a better prognostic outcome. Speech data can serve as a window into cogni...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Zehra Shah [verfasserIn] Jeffrey Sawalha [verfasserIn] Mashrura Tasnim [verfasserIn] Shi-ang Qi [verfasserIn] Eleni Stroulia [verfasserIn] Russell Greiner [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	speech and audio classification pathological speech and language automatic analysis of speaker states and traits machine learning natural language proceeding (NLP)

Übergeordnetes Werk:	In: Frontiers in Computer Science - Frontiers Media S.A., 2019, 3(2021)
Übergeordnetes Werk:	volume:3 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fcomp.2021.624659

Katalog-ID:	DOAJ066400171

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callnumber-first	Q - Science
author	Zehra Shah
spellingShingle	Zehra Shah misc QA75.5-76.95 misc speech and audio classification misc pathological speech and language misc automatic analysis of speaker states and traits misc machine learning misc natural language proceeding (NLP) misc Electronic computers. Computer science Learning Language and Acoustic Models for Identifying Alzheimer’s Dementia From Speech
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topic_title	QA75.5-76.95 Learning Language and Acoustic Models for Identifying Alzheimer’s Dementia From Speech speech and audio classification pathological speech and language automatic analysis of speaker states and traits machine learning natural language proceeding (NLP)
topic	misc QA75.5-76.95 misc speech and audio classification misc pathological speech and language misc automatic analysis of speaker states and traits misc machine learning misc natural language proceeding (NLP) misc Electronic computers. Computer science
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title	Learning Language and Acoustic Models for Identifying Alzheimer’s Dementia From Speech
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title_full	Learning Language and Acoustic Models for Identifying Alzheimer’s Dementia From Speech
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title_sort	learning language and acoustic models for identifying alzheimer’s dementia from speech
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title_auth	Learning Language and Acoustic Models for Identifying Alzheimer’s Dementia From Speech
abstract	Alzheimer’s dementia (AD) is a chronic neurodegenerative illness that manifests in a gradual decline of cognitive function. Early identification of AD is essential for managing the ensuing cognitive deficits, which may lead to a better prognostic outcome. Speech data can serve as a window into cognitive functioning and can be used to screen for early signs of AD. This paper describes methods for learning models using speech samples from the DementiaBank database, for identifying which subjects have Alzheimer’s dementia. We consider two machine learning tasks: 1) binary classification to distinguish patients from healthy controls, and 2) regression to estimate each subject’s Mini-Mental State Examination (MMSE) score. To develop models that can use acoustic and/or language features, we explore a variety of dimension reduction techniques, training algorithms, and fusion strategies. Our best performing classification model, using language features with dimension reduction and regularized logistic regression, achieves an accuracy of 85.4% on a held-out test set. On the regression task, a linear regression model trained on a reduced set of language features achieves a root mean square error (RMSE) of 5.62 on the test set. These results demonstrate the promise of using machine learning for detecting cognitive decline from speech in AD patients.
abstractGer	Alzheimer’s dementia (AD) is a chronic neurodegenerative illness that manifests in a gradual decline of cognitive function. Early identification of AD is essential for managing the ensuing cognitive deficits, which may lead to a better prognostic outcome. Speech data can serve as a window into cognitive functioning and can be used to screen for early signs of AD. This paper describes methods for learning models using speech samples from the DementiaBank database, for identifying which subjects have Alzheimer’s dementia. We consider two machine learning tasks: 1) binary classification to distinguish patients from healthy controls, and 2) regression to estimate each subject’s Mini-Mental State Examination (MMSE) score. To develop models that can use acoustic and/or language features, we explore a variety of dimension reduction techniques, training algorithms, and fusion strategies. Our best performing classification model, using language features with dimension reduction and regularized logistic regression, achieves an accuracy of 85.4% on a held-out test set. On the regression task, a linear regression model trained on a reduced set of language features achieves a root mean square error (RMSE) of 5.62 on the test set. These results demonstrate the promise of using machine learning for detecting cognitive decline from speech in AD patients.
abstract_unstemmed	Alzheimer’s dementia (AD) is a chronic neurodegenerative illness that manifests in a gradual decline of cognitive function. Early identification of AD is essential for managing the ensuing cognitive deficits, which may lead to a better prognostic outcome. Speech data can serve as a window into cognitive functioning and can be used to screen for early signs of AD. This paper describes methods for learning models using speech samples from the DementiaBank database, for identifying which subjects have Alzheimer’s dementia. We consider two machine learning tasks: 1) binary classification to distinguish patients from healthy controls, and 2) regression to estimate each subject’s Mini-Mental State Examination (MMSE) score. To develop models that can use acoustic and/or language features, we explore a variety of dimension reduction techniques, training algorithms, and fusion strategies. Our best performing classification model, using language features with dimension reduction and regularized logistic regression, achieves an accuracy of 85.4% on a held-out test set. On the regression task, a linear regression model trained on a reduced set of language features achieves a root mean square error (RMSE) of 5.62 on the test set. These results demonstrate the promise of using machine learning for detecting cognitive decline from speech in AD patients.
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title_short	Learning Language and Acoustic Models for Identifying Alzheimer’s Dementia From Speech
url	https://doi.org/10.3389/fcomp.2021.624659 https://doaj.org/article/5b94191aa49d4d809ec3e2fefeeab3a4 https://www.frontiersin.org/articles/10.3389/fcomp.2021.624659/full https://doaj.org/toc/2624-9898
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up_date	2024-07-03T19:50:55.769Z
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Learning Language and Acoustic Models for Identifying Alzheimer’s Dementia From Speech

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