Detection of articulation disorders using empirical mode decomposition and neural networks

Authors:
George Georgoulas;Voula C. Georgopoulos;George D. Stylios;Chrysostomos D. Stylios
Affiliations:
Dept of Computer Applications in Finance and Management, TEl of Ionian Islands, Lefkas, Greece;Dept. of Speech and Language Therapy, TEl of Patras, Koukouli Patras, Greece;Dept of Computer Applications in Finance and Management, TEl of Ionian Islands, Lefkas, Greece;Laboratory of Knowledge and Intelligent Computing, Dept. of Informatics and Communications Technology, TEl of Epirus, Kostakioi, Greece
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

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Abstract

This paper introduces a novel approach based on signal processing methods to extract features from speech signals and based on them to detect a specific type of articulation disorders. Articulation, in effect, is the specific and characteristic way that an individual produces the speech sounds. Empirical Mode Decomposition and the Hilbert Huang transform are applied to the speech signal in order to calculate the marginal spectrum of the signal. The marginal spectrum is subsequently subject to a mel-cepstrum like processing to extract features which are fed to a neural network classifier responsible for the identification of the articulation disorder. Our preliminary results suggest that this approach is very promising for the detection of the disorder under study.