The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Neurolinguistic and acoustic analysis of articulatory impairments in Arabic speech disorders
Open accessAbstract
Background: Pharyngeal sounds, integral to Arabic phonetics, require precise vocal tract coordination, posing significant challenges for individuals with speech disorders. Objective: This study investigates the neurolinguistic and acoustic characteristics of pharyngeal sound production in Arabic speakers with speech impairments, aiming to elucidate the impact of neurological disruptions on articulatory precision. Methods: A comparative study was conducted with 20 participants (10 with speech disorders, 10 with typical speech). Acoustic analysis, including spectrographic evaluation, was used to quantify deviations in pharyngeal sound production. Concurrently, neurolinguistic assessments, which included neurological evaluations, identified disruptions in neural pathways governing speech motor control. Results: Individuals with speech disorders exhibited significant neuromotor deficits, correlating with distinct acoustic deviations in pharyngeal sound production. These findings highlight the synergy of neurolinguistic and acoustic approaches in identifying underlying mechanisms of speech impairment. Conclusion: By integrating neurolinguistic and acoustic analyses, this study establishes a novel framework for diagnosing and treating pharyngeal sound disorders in Arabic speakers. The results inform targeted therapeutic interventions and the development of assistive technologies, advancing clinical outcomes in speech-language pathology.
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“The limitation of existing acoustic-based approaches such as NAA and TMTLN for simultaneously characterizing the issues related to articulation, cognition, and behavior associated with speech disorders is because of their focus on low-level speech abnormalities while failing to consider neurolinguistic inconsistency related to semantics.”
From NSX-Net: A Neurolinguistic and Acoustic Multimodal Deep Learning Framework for Speech Disorder Classification (2026) · cites this paper as [21]
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Cited by
Sorted by citations
Nadeem, A., Khan, M. Z., Sirshar, M., & Thirunavukkarasu, U. (2026) · Diagnostics · doi:10.3390/diagnostics16152377
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Metadata
- Authors
- Algaraady, J., Mahyoob, M., & Khan, M. Z.
- Affiliations
- Taiz University (YE)
- Taibah University (SA)
- Corresponding author
- Algaraady, Jeehaan
- Mahyoob, Mohammad
- Venue
- Frontiers in Human Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2025-10-07
- Volume
- 19
- Pages
- 1638363-1638363
- ISSN
- 1662-5161 (ISSN-L)
- PMC
- PMC12537716
- OpenAlex
- W4414879916
- Cited by
- 2
- References
- 25
- Citations by year
- 2026: 2
- FWCI
- 1.29
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Human Neuroscience (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- King Salman Center for Disability Research (KSRG-2024-141)
- Topics
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Phonetics and Phonology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Keywords
- speech motor control, neuromotor deficits, speech acoustics, spectrographic analysis, speech-language pathology
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