Automated Classification of Phonetic Segments in Child Speech Using Raw Ultrasound Imaging

Al Ani, S., Cleland, J. and Zoha, A. (2024) Automated Classification of Phonetic Segments in Child Speech Using Raw Ultrasound Imaging. In: 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOIMAGING 2024), Rome, Italy, 21-23 February 2024, pp. 326-331. ISBN 9789897586880 (doi: 10.5220/0000184700003657)

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.

• Item Type: Conference Proceedings
• Keywords: Ultrasound tongue imaging, child speech, texture descriptor, convolutional neural networks.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zoha, Dr Ahmed and Al Ani, Saja
• Authors: Al Ani, S., Cleland, J., and Zoha, A.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• ISSN: 2184-4305
• ISBN: 9789897586880
• Copyright Holders: Copyright © 2024 SCITEPRESS – Science and Technology Publications, Lda.
• First Published: First published in Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2024) - Volume 1, pp. 326-331
• Publisher Policy: Reproduced under a Creative Commons License

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