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Approach for spectrogram analysis in detection of selected pronunciation pathologies

  • Wojciech BoduszEmail author
  • Zuzanna Miodońska
  • Paweł Badura
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 623)

Abstract

An attempt to automatise selected pronunciation pathology detection in preschool children is described in this paper. Consonant [Z] in various phonetic surroundings is taken into consideration as eventual sigmatism indicator. The analysis involves spectrogram analysis in terms of image processing methods used for feature extraction and classification. Five dedicated features are defined and extracted, i.a., from a frequency sub-band of [1500, 6500] Hz. Binary classification using support vector machine enables pathology detection. The system performance is evaluated using sensitivity, specificity, and accuracy metrics in two cross-validation experiments over a database of 140 speech recordings with 50 normative and 90 pathological cases. Repeatable efficiency metrics at a ca. 85% level confirm the method capabilities and encourage to develop the system for the speech diagnosis support.

Keywords

speech pathology spectrogram analysis image processing 

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Notes

Acknowledgements

This research was supported by the Polish Ministry of Science and Silesian University of Technology statutory financial support No. BK-200/RIB1/2017.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Wojciech Bodusz
    • 1
    Email author
  • Zuzanna Miodońska
    • 1
  • Paweł Badura
    • 1
  1. 1.Faculty of Biomedical EngineeringSilesian University of TechnologyZabrzePoland

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