Test 3F is used to diagnose the extent of motor speech disorder – dysarthria for czech speakers. The evaluation of dysarthric speech is distorted by subjective assessment. The motivation behind this thesis is that there are not many automatic and objective analysis tools that can be used to evaluate phonation, articulation, prosody and respiration of speech disorder. The aim of this diploma thesis is to identify, implement and test acoustic features of speech that could be used to objectify and automate the evaluation. These features should be easily interpretable by the clinician. It is assumed that the evaluation could be more precise because of the detailed analysis that acoustic features provide. The performance of these features was tested on database of 151 czech speakers that consists of 51 healthy speakers and 100 patients. Statistical analysis and methods of machine learning were used to identify the correlation between features and subjective assesment. 27 of total 30 speech tasks of Test 3F were identified as suitable for automatic evaluation. Within the scope of this thesis only 10 tasks of Test 3F were tested because only a limited part of the database could be preprocessed. The result of statistical analysis is 14 features that were most useful for the test evaluation. The most significant features are: MET (respiration), relF0SD (intonation), relSEOVR (voice intensity – prosody). The lowest prediction error of the machine learning regression models was 7.14 %. The conclusion is that the evaluation of most of the tasks of Test 3F can be automated. The results of analysis of 10 tasks shows that the most significant factor in dysarthria evaluation is limited expiration, monotone voice and low variabilty of speech intensity.