This study is supported by the collective project of Department of Circuit Theory in FEE-CTU in Prague and the Department of Paediatric Neurology in 2nd Faculty of Medicine of Charles University in Prague. One of the interests in paediatric neurology is a research on electroclinical syndromes area combined with speech disorders. The aim of our project is, among others, finding a connectivity between children's neurological disorders called developmental dysphasia [2], [3] and the assessment of the degree of perception and impairment of speech. From the point of view the characterisation of language, it is very complicated to determine relevant and irrelevant information about speech and to connect it with a searching target. That is why a part of the project is solved by artificial neural networks (ANNs) with using knowledge of phonetics.
At first the analysis of vowels was researched using the ANN. An initial hypothesis says that developmental dysphasia can influence a shift of formant frequencies in spectral characteristics compared with the formant frequencies of healthy children.
It is necessary to have a comparative voice analysis of healthy children for evaluating the degree of these modifications. Our team created the healthy and ill children's speech databases with a comparative corpus. The healthy children's speech was recorded at kindergartens and on the first level of elementary school. The ill children's speech was recorded at hospital. The children were from 4 to 10 years old. The comparative corpus, which includes isolated vowels, monosyllables and polysyllables, was compiled by neurological specialists as related to medical therapy. The same corpus was used for the comparative analysis of healthy children. Our aim is a vowel recognition and visualisation by a Supervised Self-Organizing Maps - Supervised SOMs, which represent one of the types of the ANNs with better cluster separation based on the Kohonen map, see [1]. Better cluster separation is useful for the visualisation analysis, which is easy for the current user. The Recognition Rate (RR) depends also on the knowledge of the children's voice evolution regularity related to their age and gender. Our main objective is not the highest RR, but to observe its trend. We assume that wrong mapped vowels should be one of the indicators of developmental dysphasia.
The application of the Supervised SOM should prove the ability not only to discriminate healthy and ill children, but also to describe a trend of the neurological disorders with the assistance of repeated three-month recordings during a medical therapy.
The method described in the following text was developed to analyze disordered children speech. The diagnosis of the children is developmental dysphasia. Since developmental dysphasia has impact on children's speech ability, the classification of utterances helps to determine whether treatment and medication are appropriate. The paper describes the method developed to provide classification based on utterances but without any additional demands on speech preprocessing (e.g. labeling). The method uses Matching Pursuit algorithm for speech parameterization and Kohonen Self-Organizing Maps for extraction of features from utterances. Features extracted from the utterances of healthy children are then compared to features obtained from the speech of children suffering from the illness.