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Patients suffering from Parkinson's disease must periodically undergo a series of tests, usually performed at medical facilities, to diagnose the current state of the disease. Parkinson's disease progression assessment is an important set of procedures that supports the clinical diagnosis. A common part of the diagnos- tic train is analysis of speech signal to identify the disease-specific communication issues. This contribution describes two types of computational models that map speech signal measurements to clinical outputs. Speech signal samples were ac- quired through measurements from patients suffering from Parkinson's disease. In addition to direct mapping, the developed systems must be able of generaliza- tion so that correct clinical scale values can be predicted from future, previously unseen speech signals. Computational methods considered in this paper are arti-ficial neural networks, particularly feedforward networks with several variants of backpropagation learning algorithm, and adaptive network-based fuzzy inference system (ANFIS). In order to speed up the learning process, some of the algorithms were parallelized. Resulting diagnostic system could be implemented in an embed- ded form to support individual assessment of Parkinson's disease progression from patients' homes. [ABSTRACT FROM AUTHOR]