This paper addresses the processing of speech data and their utilization in a decision support system. The main aim of this work is to utilize machine learning methods to recognize pathological speech, particularly dysphonia. We extracted 1560 speech features and used these to train the classification model. As classifiers, three state-of-the-art methods were used: K-nearest neighbors, random forests, and support vector machine. We analyzed the performance of classifiers with and without gender taken into account. The experimental results showed that it is possible to recognize pathological speech with as high as a 91.3% classification accuracy.

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In this study we address the issue of the handwriting processing by extracting parameters from the written speech. The work applies machine learning method-the decision trees method which aims to recognize the impaired handwriting, particularly dysgraphia. 55 features (e.g. total time, pen movement, pressure, speed, acceleration) were extracted from each out of 80 handwriting samples while analyzing the performance of classifier for the dominant parameter-minimal speed, and without the dominant parameter as well. The experimental results of the classifier are compared to the results of the statistical test-Mann Whitney U-test as a complex and challenging endeavor to create an accurate classification.

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