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Related Experiment Videos

Automatic Screening to Detect 'At Risk' Child Speech Samples using a Clinical Group Verification framework.

Prasanna V Kothalkar, Johanna Rudolph, Christine Dollaghan

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |November 17, 2018
    PubMed
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    This study shows that speech processing techniques can accurately detect speech sound disorders (SSD) in young children. Early detection through this method aids timely intervention, improving developmental outcomes.

    Area of Science:

    • Speech-language pathology
    • Computational linguistics
    • Pediatric audiology

    Background:

    • Pediatric speech sound disorders (SSD) impact education and social development.
    • Early SSD detection is crucial but challenging due to developmental variability.
    • Existing screening methods may not fully capture speech production nuances in young children.

    Purpose of the Study:

    • To explore the feasibility of using computational methods for early detection of pediatric speech sound disorders.
    • To develop novel speech processing techniques for analyzing children's speech samples.
    • To assess the accuracy of these techniques in identifying children at risk for SSD.

    Main Methods:

    • Gaussian Mixture Models were applied to analyze speech samples from children aged 3-6 years.

    Related Experiment Videos

  • Speech samples were collected by parents via an iOS application.
  • Speech-language pathologists provided expert classification of speech samples as 'at risk' or 'no risk' for SSD.

    • Main Results:

    • Novel distance measures and group scoring techniques demonstrated good subject-level prediction accuracy.
    • The developed methods show promise in distinguishing between typical and atypical speech production.
    • Computational analysis of speech samples achieved effective screening for potential SSD.

    Conclusions:

    • Speech processing and speaker verification techniques show potential for modeling and screening pediatric speech sound disorders.
    • This approach may offer a scalable and objective method for early SSD identification.
    • Further research can refine these computational tools for widespread clinical application.