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:	Jacqueline McKechnie, Mostafa Shahin, Beena Ahmed, Patricia McCabe, Joanne Arciuli, Kirrie J. Ballard
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	childhood apraxia of speech motor speech disorder prosody lexical stress automatic speech recognition diagnosis Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/9074669058b6492e8f68735468b0a6aa
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spelling	oai:doaj.org-article:9074669058b6492e8f68735468b0a6aa2021-11-25T16:56:41ZAn Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech10.3390/brainsci111114082076-3425https://doaj.org/article/9074669058b6492e8f68735468b0a6aa2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3425/11/11/1408https://doaj.org/toc/2076-3425Childhood 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., <b>di</b>nosaur) or WS (e.g., ba<b>na</b>na) 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.Jacqueline McKechnieMostafa ShahinBeena AhmedPatricia McCabeJoanne ArciuliKirrie J. BallardMDPI AGarticlechildhood apraxia of speechmotor speech disorderprosodylexical stressautomatic speech recognitiondiagnosisNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENBrain Sciences, Vol 11, Iss 1408, p 1408 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	childhood apraxia of speech motor speech disorder prosody lexical stress automatic speech recognition diagnosis Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	childhood apraxia of speech motor speech disorder prosody lexical stress automatic speech recognition diagnosis Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Jacqueline McKechnie Mostafa Shahin Beena Ahmed Patricia McCabe Joanne Arciuli Kirrie J. Ballard An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech
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., <b>di</b>nosaur) or WS (e.g., ba<b>na</b>na) 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.
format	article
author	Jacqueline McKechnie Mostafa Shahin Beena Ahmed Patricia McCabe Joanne Arciuli Kirrie J. Ballard
author_facet	Jacqueline McKechnie Mostafa Shahin Beena Ahmed Patricia McCabe Joanne Arciuli Kirrie J. Ballard
author_sort	Jacqueline McKechnie
title	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_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_sort	automated lexical stress classification tool for assessing dysprosody in childhood apraxia of speech
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/9074669058b6492e8f68735468b0a6aa
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An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech

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