Abstract
Sigmatism, or lisp, is a common speech pathology defined by the misarticulation of sibilants and commonly appears in preschool-age children. Automated diagnosis from speech data has been used for other disorders, and the use of acoustic features could objectify the diagnosis procedure. 1593 multichannel recordings from 85 young children were subjected to feature extraction and classification using a neural network. The classification performance was evaluated for single and multichannel input as well as multiple feature sets and articulation phases. Multichannel recordings increased the classifier accuracy from 78.75% to 87.27% when using cepstral and spectral features. The introduction of a multichannel acoustic features was shown to increase sigmatism detection accuracy.
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Acknowledgement
The work has been partially financed by: Polish Ministry of Science and Silesian University of Technology statutory financial support for young researchers BKM-510/RAu-3/2017 and Faculty of Biomedical Engineering statutory financial support No. BK-209/RIB1/2018 (07/010/BK_18/0021).
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Woloshuk, A., Kręcichwost, M., Miodońska, Z., Badura, P., Trzaskalik, J., Pietka, E. (2019). CAD of Sigmatism Using Neural Networks. 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_23
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DOI: https://doi.org/10.1007/978-3-319-91211-0_23
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