  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.
