## 8 Discussion
Due to the specificity of the research assumption (fusion of features using different types of speech), as well as assuming that the acquired samples came from patients in the ON phase, the results obtained can be compared with the results presented in [103; 105; 106; 107] with a similar methodology and performance metric. In [107], data are collected via a physician's examination process that is very similar to our experiment. It included multimodal data acquisition (speech and writing) and the registration included different types of voice samples [107]. Here, the SVM and k-nn algorithms were applied for this process, the use of which also gave other authors the best results. The SVM classifier produced higher accuracies than the k-NN classifier. The experimental results point out that collecting as many voice samples as possible from patients and extracting the features of each voice sample with different metrics increase the success of the diagnostic system. In [105], the authors use the traditional pipeline approach; SVM classifiers were developed using different combinations of the baseline and glottal features. Among the three baseline feature sets, the articulation features show the best detection result. The results of this research conform with approach proposed in this article. Detection results are observed after combining the feature sets. In [106], three different feature sets are computed based on phonation, articulation, and prosody analysis using an SVM and CNN. The speech of the patients is captured during a phone call using smartphones during spontaneous conversations. Several feature sets are computed to assess the phonation, articulation, and prosody impairments. For the case of the SVM, the accuracies range from 58% to 75% in the original phone calls. The results improve in up \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classifier** & \multicolumn{3}{c}{**1-nn**} & \multicolumn{3}{c}{**SVM**} \\ \hline  & **ACC [\%]** & **Se [\%]** & **Sp [\%]** & **ACC [\%]** & **Se [\%]** & **Sp [\%]** \\ \hline Fusion model & & & & & & \\ based on pre-selection & 83.3 & 70.8 & 95.8 & 79.1 & 62.5 & 95.8 \\ \hline AUC & & 0.85 & & & 0.82 & \\ \hline Fusion model & & & & & & \\ based on final & 92.2 & 91.1 & 93.3 & 89.6 & 95.8 & 83.3 \\ selection & & & & & & \\ \hline AUC & & 0.89 & & & 0.92 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Effectiveness of classifiers for modality integration with selection of features. to 21% (absolute) when the recordings are processed with the SE algorithm. In [103], the results in terms of accuracy are pretty high. The methodology for determining features is different. The authors do not report whole factors of identification. Noisy environment means that speech samples were recorded in a clinic, in the examination room. They treated it an inherently noisy environment, with no prior measures used for soundproofing and noise reduction. Speech denoising was achieved in the context of parkinsonian speech identification using an optimal Wiener filter. In our research, a clinic room is considered to be a normal condition for recording. We do not use additional filtering. The original signal gives 87% as the best record of accuracy. Table 8 shows a comparison of the accuracy, sensitivity and specificity values obtained in this paper and by other authors. The accuracy of the diagnosis reported here was obtained at a higher level with a much smaller number of features.