CAD of Sigmatism Using Neural Networks
| Autor: | Michal Krecichwost, Zuzanna Miodońska, Ewa Pietka, Pawel Badura, Joanna Trzaskalik, Andre Woloshuk |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
medicine.medical_specialty Artificial neural network Computer science Speech recognition Feature extraction CAD 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Cepstrum medicine Lisp Speech-Language Pathology computer Classifier (UML) 030217 neurology & neurosurgery computer.programming_language |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783319912103 ITIB |
| DOI: | 10.1007/978-3-319-91211-0_23 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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