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Support-Vector Machine-Based Classifier of Cross-Correlated Phoneme Segments for Speech Sound Disorder Screening.

Emilian-Erman Mahmut1, Stelian Nicola1, Vasile Stoicu-Tivadar1

  • 1Department of Automation and Applied Informatics, Politehnica University Timisoara, Romania.

Studies in Health Technology and Informatics
|May 25, 2022
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Summary

An automated Speech Sound Disorder (SSD) screening tool uses Support-Vector Machine (SVM) classification for phoneme segments, achieving 97.5% accuracy. This technology aids Speech-Language Pathologists (SLPs) in early detection.

Keywords:
Speech Sound DisordersSupport-Vector Machinecross-correlation

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Area of Science:

  • Computer Science
  • Linguistics
  • Speech Pathology

Background:

  • Speech Sound Disorders (SSDs) are increasingly prevalent in 5-6 year olds.
  • The COVID-19 pandemic and specialist shortages necessitate innovative screening solutions.
  • Automated tools can significantly support Speech-Language Pathologists (SLPs).

Purpose of the Study:

  • To develop an automated screening tool for Speech Sound Disorders (SSDs).
  • To classify cross-correlated phoneme segments using Support-Vector Machine (SVM) algorithms.
  • To enhance the efficiency and accessibility of SSD screening.

Main Methods:

  • Utilizing cross-correlation for phoneme segmentation from audio samples.
  • Employing Support-Vector Machine (SVM) for classification of segmented phonemes.
  • Developing a pre-processing algorithm for data extraction and feature engineering.

Main Results:

  • The SVM-based classification achieved a high accuracy of 97.5%.
  • The method was tested on a dataset comprising 132 rows.
  • The algorithm successfully segmented and classified target phonemes.

Conclusions:

  • The developed SVM method shows significant promise for an automated SSD screening tool.
  • This approach can assist in early identification of SSDs, especially in resource-limited settings.
  • The tool offers a scalable solution to support SLPs and address the growing need for SSD assessment.