Artificial neural networks in the disabled speech analysis

Abstract

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.