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Feasibility of Technology Enabled Speech Disorder Screening.

Andreas Duenser1, Lauren Ward1, Alessandro Stefani1

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Early detection of childhood speech disorders is crucial. This study developed a prototype tool using machine learning for rapid speech disorder screening in children, aiding early intervention and improving outcomes.

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Area of Science:

  • Pediatric Health
  • Speech Pathology
  • Computational Linguistics

Background:

  • Speech disorders affect 1 in 20 Australian children, impacting literacy and academic success.
  • Early identification of speech disorders is vital to mitigate long-term educational and social costs.
  • Current screening methods may lack accessibility and efficiency for widespread early detection.

Purpose of the Study:

  • To develop and test a prototype screening and decision support tool for assessing speech disorders in young children.
  • To leverage technology for automated phoneme classification, distinguishing normal from disordered speech.
  • To lay the groundwork for a mobile application for accessible, early speech disorder screening.

Main Methods:

  • Development of a prototype tool integrating speech signal processing and machine learning algorithms.
  • Utilizing expert knowledge alongside AI for automatic classification of phonemes.
  • Conducting feasibility tests to evaluate the prototype's performance in assessing speech disorders.

Main Results:

  • Successful development of a functional prototype for speech disorder assessment.
  • Demonstrated capability of the prototype to automatically classify phonemes.
  • Feasibility tests indicated potential for accurate and efficient speech disorder screening.

Conclusions:

  • The developed prototype shows promise for early and accessible speech disorder screening.
  • Further development towards a mobile tool could significantly enhance early detection rates.
  • Automated speech analysis holds potential to reduce the burden of speech disorders on children and society.