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Related Experiment Videos

Modelling acquired dyslexia: a software tool for developing grapheme-phoneme correspondences.

C L D'Autrechy1, J A Reggia, R S Berndt

  • 1Department of Computer Science, University of Maryland, College Park 20742.

Proceedings. Symposium on Computer Applications in Medical Care
|January 1, 1991
PubMed
Summary

A new computer program segments English words into character groups that correspond to speech sounds, aiding dyslexia research. This method efficiently processes large word sets for improved reading models.

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Area of Science:

  • Computational linguistics
  • Cognitive psychology
  • Speech processing

Background:

  • Developing computational models for acquired dyslexia requires accurate mapping between written words and spoken phonemes.
  • Existing methods for grapheme-phoneme correspondence may not provide a unique segmentation for all English words.

Purpose of the Study:

  • To develop a method for grouping printed characters in English words to achieve a one-to-one correspondence with phonemes.
  • To create a program that automates the segmentation of words based on derived correspondences.

Main Methods:

  • Derived a set of correspondences for legal character groupings and their associations with phonemes.
  • Developed a segmentation program utilizing interchangeable correspondences to process English words.

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  • Applied the program to a large corpus of 20,000 words, tabulating segmentation success.

    • Main Results:

    • The developed program successfully segmented a 20,000-word corpus.
    • The approach demonstrated effectiveness and efficiency in achieving character-to-phoneme segmentation.
    • A single, consistent segmentation was yielded for words based on the derived correspondences.

    Conclusions:

    • The developed segmentation program and approach are effective and efficient for mapping printed characters to phonemes.
    • This method provides a viable solution for enhancing computer models of acquired dyslexia.
    • Automated grapheme-phoneme segmentation is feasible and beneficial for linguistic research.