Summary. Hidden Markov Models are widely used for recognition of any patterns appearing in an input signal. In the work HMM's were used to recognize two kind of speech disorders in an acoustic signal: prolongation of fricative phonemes and blockades with repetition of stop phonemes.In the work a tests results of a recognition effectiveness are presented for considered speech disorders by HMM models in different configurations. There were summary models applied for a class of disorder recognition, as well as models related to disturbance of individual phoneme. The tests were carried out by use of the author's implementation of HMM procedures.

The study presents the comparison of the effects of echo transmitted via single and combined channels (auditory, visual and tactile) on the speech of stutterers. The dependence of stuttering intensity and speech velocity upon echo delay time was determined. For all transmission channels the stuttering intensities and the speech velocities decreased with the increase in the delay time of the echo. The results were analyzed statistically by means of the ANOVA method. It was proven that the corrective effects of visual echo and tactile echo were comparable. Echo transmitted via the auditory channel was more effective than when transmitted via the visual or tactile channels. The greatest efficiency could be observed by transmitting echo via three connected channels: auditory, visual and tactile. The results obtained show that in stuttering therapy it is justified to use echo transmitted via three connected channels (auditory, visual, tactile).

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This work covers the problem of application of neural networks to recognition and categorization of nonfluent and fluent utterance records. Fifty-five 4-s speech samples where the blockade on plosives (p, b, t, d, k and g) occurred and 55 recordings of speech of fluent speakers containing the same fragments were applied. Two Kohonen networks were used. The purpose of the first network was to reduce the dimension of the vector describing the input signals. A result of the analysis was the output matrix consisting of the neurons winning in a particular time frame. This matrix was taken as an input for the next selforganizing map network. Various types of Kohonen networks were examined with respect to their ability to classify utterances correctly into two, non-fluent and fluent, groups. Good examination results were accomplished and classification correctness exceeded 76%.