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Evaluation of Puberty in Girls by Spectral Analysis of Voice

Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 762)

Abstract

In this paper, a method for girls’ pubertal status evaluation is presented. The proposed algorithm uses voice features. Spectral analysis, Support Vector Machine and Random Forest Trees were employed. The obtained results are promising. Sensitivity reached 89.38%, when all features were included in the calculations (SVM). The highest specificity was achieved when only standard deviations were used (80.14% for the RF). Accuracy was greater than 80% for both classifiers when all features were used.

Keywords

Voice analysis Spectral analysis Puberty evaluation 
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Notes

Acknowledgement

We would like to thank Andre Woloshuk for his English language corrections.

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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Biomedical EngineeringSilesian University of TechnologyZabrzePoland
  2. 2.Department of AnthropologyWroclaw University of Environmental and Life SciencesWrocławPoland

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