Instrumental and Machine Learning Analysis Approaches to Speech Sound Disorder Assessment in Arabic-Speaking Children: A Review With Narrative Synthesis.
Purpose: This review article provides a narrative synthesis of instrumental and machine learning (ML) approaches used to study speech sound development and disorders, with emphasis on Arabic-speaking children. Research in this area draws on multiple disciplines, including acoustic and articulatory p...
| Publicado en: | Perspectives of the ASHA Special Interest Groups Vol. 11; no. 3; pp. 1141 - 1160 |
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| Autor principal: | |
| Formato: | glossary review tables/charts Journal Article |
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American Speech-Language-Hearing Association
Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194459204&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194459204 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2381473X KTSD jtl: Perspectives of the ASHA Special Interest Groups issn: 2381473X maglogo: N pubinfo: dt: Jun2026 vid: 11 iid: 3 pid: 42 pub: American Speech-Language-Hearing Association place: Rockville, Maryland artinfo: ui: 194459204 194459204 194459204 10.1044/2026_PERSP-25-00135 194459204 ppf: 1141 ppct: 19 formats: fmt: @attributes: type: P tig: atl: Instrumental and Machine Learning Analysis Approaches to Speech Sound Disorder Assessment in Arabic-Speaking Children: A Review With Narrative Synthesis. aug: au: Abdulkader, Dalia M. affil: Department of Rehabilitation Health Sciences-Speech and Hearing Therapy Program, King Saud University, Riyadh, Saudi Arabia sug: subj: Articulation Disorders In Infancy and Childhood Machine Learning Language Development In Infancy and Childhood Speech and Language Assessment In Infancy and Childhood Speech Acoustics Electropalatography Language Tests Neural Networks (Computer) Child Development Communication Child Child: 6-12 years ab: Purpose: This review article provides a narrative synthesis of instrumental and machine learning (ML) approaches used to study speech sound development and disorders, with emphasis on Arabic-speaking children. Research in this area draws on multiple disciplines, including acoustic and articulatory phonetics, computer science, linguistics, psychology, and clinical speech-language pathology. The review aims to summarize available methodologies and highlight considerations for applying them in Arabic-speaking contexts. Method: Instrumental and computational methods reviewed include acoustic analysis, electropalatography, ultrasound tongue imaging, computerized analysis tools (e.g., Phon, Computerized Language Analysis, Praat), and ML-based techniques such as probabilistic models and neural networks. Key research designs and strategies commonly employed in the study of speech sound disorders (SSDs) are also discussed, with references to representative studies. Conclusions: This review outlines the methodological options available to researchers and clinicians, emphasizing how each contributes to accurate and culturally responsive SSD assessment. Traditional perceptual approaches remain central but benefit from integration with instrumental and computational methods, which provide objective data and scalable solutions. For Arabic-speaking children, the development of dialect-sensitive corpora and normative data sets is essential to ensure diagnostic validity. By synthesizing current methods, this review informs both research design and clinical practice, supporting more equitable and linguistically appropriate assessment of SSDs. pubtype: Academic Journal doctype: glossary review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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