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.
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We would like to thank Andre Woloshuk for his English language corrections.
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Bugdol, M.D., Bieńkowska, M.J., Bugdol, M.N., Lipowicz, A.M., Mitas, A.W., Wijata, A. (2019). Evaluation of Puberty in Girls by Spectral Analysis of Voice. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2018. Advances in Intelligent Systems and Computing, vol 762. Springer, Cham. https://doi.org/10.1007/978-3-319-91211-0_21
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