@inproceedings{d3470201c52944c0b47ce9b157ca919a,
title = "Convolutional Neural Networks for Computer Aided Diagnosis of Interdental and Rustling Sigmatism",
abstract = "Sigmatism (lisping), is the misarticulation of sibilant sounds. Multiple classes of sigmatism exist, and the treatment for each type differs. An automatic classifier may improve therapeutic options with the supervision of a speech therapist. A database containing 1188 multichannel recordings of children diagnosed as having normative pronunciation, interdental sigmatism, or rustling sigmatism was used to create visual representations of the spectrum, mel-filter bank energies (FBE), and mel-frequency cepstral coefficients. These images were used to train a convolutional neural network. The network achieved a binary accuracy of 97.67\% using FBE images to distinguish between normative pronunciation and any other of the analyzed types of sigmatism.",
keywords = "Computer-aided pronunciation evaluation, Convolutional neural network, Sibilants, Sigmatism diagnosis",
author = "Andre Woloshuk and Michal Krecichwost and Zuzanna Miodonska and Dominika Korona and Pawel Badura",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 7th International Conference on Information Technology in Biomedicine, ITIB 2019 ; Conference date: 18-06-2019 Through 20-06-2019",
year = "2019",
doi = "10.1007/978-3-030-23762-2\_16",
language = "English",
isbn = "9783030237615",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "179--186",
editor = "Ewa Pietka and Pawel Badura and Jacek Kawa and Wojciech Wieclawek",
booktitle = "Information Technology in Biomedicine, 2019",
address = "Germany",
}