Automated classification of phonetic segments in child speech using raw ultrasound imaging

Tools

Al Ani, Saja and Cleland, Joanne and Zoha, Ahmed; Guarino, Maria Pedro and Hotta, Kazuhiro and Yousef, Malik and Liu, Hui and Saggio, Giovanni and Fred, Ana and Gamboa, Hugo, eds. (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. BIOSTEC . SCITEPRESS, ITA, pp. 326-331. ISBN 9789897586880 (https://www.scitepress.org/PublicationsDetail.aspx...)

[thumbnail of Al-Ani-etal-BIOSTEC-2024-Automated-classification-of-phonetic-segments-in-child-speech-using-raw-ultrasound-imaging]
Preview
 Text. Filename: Al-Ani-etal-BIOSTEC-2024-Automated-classification-of-phonetic-segments-in-child-speech-using-raw-ultrasound-imaging.pdf
Final Published Version
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (790kB)| Preview

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.

ORCID iDs

Al Ani, Saja ORCID logoORCID: https://orcid.org/0009-0001-3703-8040, Cleland, Joanne ORCID logoORCID: https://orcid.org/0000-0002-0660-1646 and Zoha, Ahmed; Guarino, Maria Pedro, Hotta, Kazuhiro, Yousef, Malik, Liu, Hui, Saggio, Giovanni, Fred, Ana and Gamboa, Hugo
CORE (COnnecting REpositories)