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An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech

Childhood apraxia of speech (CAS) commonly affects the production of lexical stress contrast in polysyllabic words. Automated classification tools have the potential to increase reliability and efficiency in measuring lexical stress. Here, factors affecting the accuracy of a custom-built deep neural...

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Autores principales: McKechnie, Jacqueline, Shahin, Mostafa, Ahmed, Beena, McCabe, Patricia, Arciuli, Joanne, Ballard, Kirrie J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615988/
https://www.ncbi.nlm.nih.gov/pubmed/34827407
http://dx.doi.org/10.3390/brainsci11111408
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author McKechnie, Jacqueline
Shahin, Mostafa
Ahmed, Beena
McCabe, Patricia
Arciuli, Joanne
Ballard, Kirrie J.
author_facet McKechnie, Jacqueline
Shahin, Mostafa
Ahmed, Beena
McCabe, Patricia
Arciuli, Joanne
Ballard, Kirrie J.
author_sort McKechnie, Jacqueline
collection PubMed
description Childhood apraxia of speech (CAS) commonly affects the production of lexical stress contrast in polysyllabic words. Automated classification tools have the potential to increase reliability and efficiency in measuring lexical stress. Here, factors affecting the accuracy of a custom-built deep neural network (DNN)-based classification tool are evaluated. Sixteen children with typical development (TD) and 26 with CAS produced 50 polysyllabic words. Words with strong–weak (SW, e.g., dinosaur) or WS (e.g., banana) stress were fed to the classification tool, and the accuracy measured (a) against expert judgment, (b) for speaker group, and (c) with/without prior knowledge of phonemic errors in the sample. The influence of segmental features and participant factors on tool accuracy was analysed. Linear mixed modelling showed significant interaction between group and stress type, surviving adjustment for age and CAS severity. For TD, agreement for SW and WS words was >80%, but CAS speech was higher for SW (>80%) than WS (~60%). Prior knowledge of segmental errors conferred no clear advantage. Automatic lexical stress classification shows promise for identifying errors in children’s speech at diagnosis or with treatment-related change, but accuracy for WS words in apraxic speech needs improvement. Further training of algorithms using larger sets of labelled data containing impaired speech and WS words may increase accuracy.
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spelling pubmed-86159882021-11-26 An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech McKechnie, Jacqueline Shahin, Mostafa Ahmed, Beena McCabe, Patricia Arciuli, Joanne Ballard, Kirrie J. Brain Sci Article Childhood apraxia of speech (CAS) commonly affects the production of lexical stress contrast in polysyllabic words. Automated classification tools have the potential to increase reliability and efficiency in measuring lexical stress. Here, factors affecting the accuracy of a custom-built deep neural network (DNN)-based classification tool are evaluated. Sixteen children with typical development (TD) and 26 with CAS produced 50 polysyllabic words. Words with strong–weak (SW, e.g., dinosaur) or WS (e.g., banana) stress were fed to the classification tool, and the accuracy measured (a) against expert judgment, (b) for speaker group, and (c) with/without prior knowledge of phonemic errors in the sample. The influence of segmental features and participant factors on tool accuracy was analysed. Linear mixed modelling showed significant interaction between group and stress type, surviving adjustment for age and CAS severity. For TD, agreement for SW and WS words was >80%, but CAS speech was higher for SW (>80%) than WS (~60%). Prior knowledge of segmental errors conferred no clear advantage. Automatic lexical stress classification shows promise for identifying errors in children’s speech at diagnosis or with treatment-related change, but accuracy for WS words in apraxic speech needs improvement. Further training of algorithms using larger sets of labelled data containing impaired speech and WS words may increase accuracy. MDPI 2021-10-25 /pmc/articles/PMC8615988/ /pubmed/34827407 http://dx.doi.org/10.3390/brainsci11111408 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
McKechnie, Jacqueline
Shahin, Mostafa
Ahmed, Beena
McCabe, Patricia
Arciuli, Joanne
Ballard, Kirrie J.
An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech
title An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech
title_full An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech
title_fullStr An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech
title_full_unstemmed An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech
title_short An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech
title_sort automated lexical stress classification tool for assessing dysprosody in childhood apraxia of speech
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8615988/
https://www.ncbi.nlm.nih.gov/pubmed/34827407
http://dx.doi.org/10.3390/brainsci11111408
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