Analysis of Speech from People with Parkinson's Disease through Nonlinear Dynamics

Different characterization approaches, including nonlinear dynamics (NLD), have been addressed for the automatic detection of PD; however, the obtained discrimination capability when only NLD features are considered has not been evaluated yet. This paper evaluates the discrimination capability of a set with ten different NLD features in the task of automatic classification of speech signals from people with Parkinson’s disease (PPD) and a control set (CS). The experiments presented in this paper are performed considering the five Spanish vowels uttered by 20 PPD and 20 people from the CS. According the results, it is possible to achieve accuracy rates of up to 76,81% considering only utterances from the vowel /i/. When features calculated from the five Spanish vowels are combined, the performance of the system is not improved, indicating that the inclusion of more NLD features to the system does not guarantee better performance.