Artificial Neural Networks in the Disabled Speech Analysis

  • Izabela Świetlicka
  • Wiesława Kuniszyk-Jóźkowiak
  • Elżbieta Smołka
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 57)


Presented work is a continuation of conducted research concerning automatic detection of disfluency in the stuttered speech. So far, the experiments covered analysis of disorders consisted in syllable repetitions and blockades before words starting with stop consonants. Introduced work gives description of an artificial neural networks application to the recognition and clustering of prolongations, which are one of the most common disfluency that appears among stuttering people.The main aim of the research was to answer a question whether it is possible to create a model built with artificial neural networks that is able to recognize and classify disabled speech. The experiment proceeded in two phases. In the first stage, Kohonen network was applied. During the second phase, two various networks were used and next evaluated with respect to their ability to classify utterances correctly into two, non-fluent and fluent, groups.


Radial Basis Function Radial Basis Function Neural Network Radial Basis Function Network Speaker Recognition Stop Consonant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Izabela Świetlicka
    • 1
  • Wiesława Kuniszyk-Jóźkowiak
    • 2
  • Elżbieta Smołka
    • 2
  1. 1.Department of PhysicsUniversity of Life SciencesLublinPoland
  2. 2.Laboratory of Biocybernetics, Institute of Computer ScienceMaria Curie-Sklodowska UniversityLublinPoland

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