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Optimization of Entropy-Based Automated Dyslalia Screening Algorithm.

Emilian Erman Mahmut1, Dorin Berian1, Michele Della Ventura2

  • 1Politehnica University Timisoara, Romania.

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|June 24, 2020
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Summary

This study developed an automated method using information entropy to detect speech sound errors in young children. The system achieved a 93.33% accuracy in identifying initial /r/ phoneme mispronunciations.

Keywords:
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Area of Science:

  • Speech-language pathology
  • Computational linguistics
  • Information theory

Background:

  • Dyslalia, a speech disorder, affects early school-age children, presenting diagnostic challenges.
  • Existing automated methods for phoneme mispronunciation detection face significant hurdles.
  • Previous research highlights the need for accurate and efficient diagnostic tools.

Purpose of the Study:

  • To present the progress of an automated system for discriminating phoneme mispronunciations in children's speech.
  • To utilize information entropy as a key metric for analyzing speech samples.
  • To address the challenges in identifying dyslalic disorders through computational methods.

Main Methods:

  • Speech samples from early school-age children were analyzed under specific experimental conditions.
  • A feature-extraction technique was employed to process the audio data.
  • Information entropy values were computed for each speech sample to quantify variations.

Main Results:

  • The automated system achieved a high classification match rate of 93.33%.
  • The highest accuracy was observed in classifying words with initial /r/ phoneme mispronunciations.
  • The study demonstrates the efficacy of information entropy in speech analysis.

Conclusions:

  • Information entropy-based discrimination shows promise for automated detection of phoneme mispronunciations.
  • The developed method offers a potential tool for early identification of dyslalic disorders.
  • Further research can refine this approach for broader clinical application.