Cargando…
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...
Autores principales: | , , , , , |
---|---|
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 |
_version_ | 1784604239229616128 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8615988 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT mckechniejacqueline anautomatedlexicalstressclassificationtoolforassessingdysprosodyinchildhoodapraxiaofspeech AT shahinmostafa anautomatedlexicalstressclassificationtoolforassessingdysprosodyinchildhoodapraxiaofspeech AT ahmedbeena anautomatedlexicalstressclassificationtoolforassessingdysprosodyinchildhoodapraxiaofspeech AT mccabepatricia anautomatedlexicalstressclassificationtoolforassessingdysprosodyinchildhoodapraxiaofspeech AT arciulijoanne anautomatedlexicalstressclassificationtoolforassessingdysprosodyinchildhoodapraxiaofspeech AT ballardkirriej anautomatedlexicalstressclassificationtoolforassessingdysprosodyinchildhoodapraxiaofspeech AT mckechniejacqueline automatedlexicalstressclassificationtoolforassessingdysprosodyinchildhoodapraxiaofspeech AT shahinmostafa automatedlexicalstressclassificationtoolforassessingdysprosodyinchildhoodapraxiaofspeech AT ahmedbeena automatedlexicalstressclassificationtoolforassessingdysprosodyinchildhoodapraxiaofspeech AT mccabepatricia automatedlexicalstressclassificationtoolforassessingdysprosodyinchildhoodapraxiaofspeech AT arciulijoanne automatedlexicalstressclassificationtoolforassessingdysprosodyinchildhoodapraxiaofspeech AT ballardkirriej automatedlexicalstressclassificationtoolforassessingdysprosodyinchildhoodapraxiaofspeech |