## 9 Conclusions  
This article involved conducting tests aimed at detecting PD based on voice changes, focusing on its selected aspects including speech phonation, articulation, and prosody and the fusion of these subsets in patients with PD. From a clinical perspective, phonation-related issues are associated with incorrect vocal fold movements or their incomplete closure. Changes in articulation are caused by reduced amplitude and speed of lip, jaw, and tongue movements. This leads to reduced stress, inaccurate consonant articulation, and even gibberish. Prosody is a sonic speech property that takes into account intonation, volume, stress, and phonation duration. For this purpose, during the first stage of this research, we defined three separately modeled PD diagnosis systems employing different types of speech signal feature. The next step was the fusion of the proposed system with different types of feature selection. The classification results were obtained when the SVM method was applied compared to the k-nearest neighbor method, applying 1-nn in general. The results obtained suggest that each of the applied techniques has the potential to be used in relation to the resulting feature vector, which describes the voices of patients with PD. Furthermore, feature fusion leads to an improvement in system performance indicators. On the one hand, it allows for a comprehensive analysis of speech signals, taking into account various aspects of generation. This, in turn, gives rise to hopes that the suggested solution will be universal. Defining a system that utilizes all aspects of speech signal generation may guarantee its uniqueness. The end result of this research is defining an ultimate feature vector based on three various speech signal modeling methods using a loose integration descriptor. With a 10-fold validation, the 1-nn method achieved a recognition rate of 92.2 with 91.1% sensitivity and 93.3 specificity. Regarding the analysis of the study results, it should be emphasized that the patients did not suspend current treatment for the duration of the experiment, which consequently meant that the recording \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **ACC [\%]** & **Se [\%]** & **Sp [\%]** & **Additional Information** \\ \hline
[107] & 85.0 & 80.0 & 90.0 & SVM methods \\
82.5 & 850 & 80.0 & K-NN classifier \\ \hline
[105] & 65.57 & 63.29 & 67.86 & Baseline features (phonation + articulation + prosody) \\
67.93 & 69.71 & 66.14 & Baseline features + Glottal (QCP) \\ \hline
[106] & 66.0 & 88.0 & 14.0 & SVM method. Telephonic speech \\
61.0 & 82.0 & 0 & The best results were obtained when the prosody \\  & 93/96/92 & - & - & Continuous speech, speech denoising \\
83/87/86 & - & - & Original signal \\ \hline this paper & 92.2\% & 91.1\% & 93.3\% & 1-nn method \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of results obtained in other works on similar topics. was conducted after taking the drug (in the so-called 'on' phase), which largely leads to reduced disease symptoms, also related to the speech apparatus. The main problem in comparing this kind of research is corpora public availability being very limited. The second problem is the language and the audio material. Different studies use different types of speech (supervised and unsupervised). Automatic detection of PD depends on the specific phonemic content contained in the utterance. It should be compared with the same language. Only a few works conduct cross-corpora trials [55; 108; 109] due to the lack of publicly available corpora in the same language. It becomes even more important when we use a wide range of speech signals to describe all aspects of speech (phonation, articulation, and prosody). The next problem is related to highly unbalanced corpora. In some databases, there is a big average age difference between patients and controls or unmatched gender. Another problem in comparing the results of the analyzed studies is related to speech tasks.