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Authors: Saja Al Ani 1 ; Joanne Cleland 2 and Ahmed Zoha 1

Affiliations: 1 James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, U.K. ; 2 School of Psychological Sciences and Health, University of Strathclyde, Glasgow, U.K.

Keyword(s): Ultrasound Tongue Imaging, Child Speech, Texture Descriptor, Convolutional Neural Networks.

Abstract: Speech sound disorder (SSD) is defined as a persistent impairment in speech sound production leading to reduced speech intelligibility and hindered verbal communication. Early recognition and intervention of children with SSD and timely referral to speech and language therapists (SLTs) for treatment are crucial. Automated detection of speech impairment is regarded as an efficient method for examining and screening large populations. This study focuses on advancing the automatic diagnosis of SSD in early childhood by proposing a technical solution that integrates ultrasound tongue imaging (UTI) with deep-learning models. The introduced FusionNet model combines UTI data with the extracted texture features to classify UTI. The overarching aim is to elevate the accuracy and efficiency of UTI analysis, particularly for classifying speech sounds associated with SSD. This study compared the FusionNet approach with standard deep-learning methodologies, highlighting the excellent improvement results of the FusionNet model in UTI classification and the potential of multi-learning in improving UTI classification in speech therapy clinics. (More)

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Paper citation in several formats:
Al Ani, S.; Cleland, J. and Zoha, A. (2024). Automated Classification of Phonetic Segments in Child Speech Using Raw Ultrasound Imaging. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING; ISBN 978-989-758-688-0; ISSN 2184-4305, SciTePress, pages 326-331. DOI: 10.5220/0012592700003657

@conference{bioimaging24,
author={Saja {Al Ani}. and Joanne Cleland. and Ahmed Zoha.},
title={Automated Classification of Phonetic Segments in Child Speech Using Raw Ultrasound Imaging},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING},
year={2024},
pages={326-331},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012592700003657},
isbn={978-989-758-688-0},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING
TI - Automated Classification of Phonetic Segments in Child Speech Using Raw Ultrasound Imaging
SN - 978-989-758-688-0
IS - 2184-4305
AU - Al Ani, S.
AU - Cleland, J.
AU - Zoha, A.
PY - 2024
SP - 326
EP - 331
DO - 10.5220/0012592700003657
PB - SciTePress

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