## 7 Results of Experiments
### Feature Integration After the Selection of Descriptors  
Table 5 and Figure 8 show the significance ranking for individual models, taking into account different feature usefulness evaluation methods. The study aimed to highlight the differences in the rankings obtained using each procedure. Additionally, the colors in the table mark five features with the best significance indicators for each of the methods. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{10}{c}{**Number of Descriptors**} \\ \cline{2-13}  & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** \\ \hline Relief & 0.059 & 0.064 & 0.054 & \(-\)0.0002 & 0.017 & 0.049 & 0.003 & 0.049 & 0.046 & 0.024 & 0.010 \\ Chi-square & 11.87 & 0.045 & 2.95 & 0.5182 & 0.704 & 3.816 & 0.704 & 3.816 & 4.939 & 1.142 & 0.518 \\ Fischer score & 0.13 & 0.067 & 0.21 & 0.0716 & 0.098 & 0.241 & 0.135 & 0.241 & 0.284 & 0.154 & 0.173 \\ F-test & 26.87 & 0.031 & 3.28 & 0.4389 & 0.619 & 4.500 & 0.619 & 4.500 & 6.286 & 1.071 & 0.438 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ranking of significance for individual models, taking into account different evaluation methods of usefulness of features. Effectiveness of classifiers for the individual modalities for full-dimensional vectors of the speech model. Figure 7: Confusion matrices after raw integrated data fusion: **(A)** SVM; **(B)** 1-nn. In this case, the subset of the most significant features for the Relief procedure contains the following attributes: 1, 2, 3, 6, and 8. Such a subset for the Chi-square contains features 1, 3, 6, 8, and 9, with features 3, 6, 8, 9, and 11 in the case of the Fischer score method. In turn, the subset for the F-test method involves descriptors 1, 3, 6, 8, and 9. The high similarity of the Chi-square and F-test methods means that the features distinguished through these techniques are almost equally arranged by significance. In the case of each of the rankings, it is possible to distinguish hypothetical subsets, where feature significance values are close to each other within a subset, while being greatly different from feature significances in subsequent subsets. When analyzing the results obtained, it can be seen that each of the methods provides a different significant feature ranking. However, it is evident that the results obtained exhibit certain similarities. Descriptors _d3_, _d6_, and _d8_ appear as significant features, narrowing down to the five most significant features for each method. Moreover, the descriptors _d1_ and _d9_ appear three times in the feature ranking. They assume the occurrence of significant features for a minimum of three out of four methods. This enabled the practical construction of a pre-selected subset in one variant due to the repeated feature acronyms for different methods. Five features defining the final feature vector were selected. The performance of different recognition methods, taking into account different types of kernel function and different types of distance in the 1-nn method, has been shown in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Classifier**} & & \\ \hline  & **ACC [\%]** & **Se [\%]** & **Sp [\%]** \\ \hline \multicolumn{4}{c}{Fusion model based on pre-selection (11 features)} \\ \hline SVM (Linear kernel) & 72.9 & 58.3 & 87.5 \\ SVM (Quadratic kernel) & 79.1 & 62.5 & 95.8 \\ SVM (Cubic kernel) & 75.0 & 58.3 & 91.7 \\ SVM (Gaussian kernel) & 77.1 & 58.3 & 95.8 \\
1-nn (Euclidean Distance) & 83.3 & 70.8 & 95.8 \\
1-nn (Chebyshev Distance) & 75.0 & 62.5 & 87.5 \\
1-nn (Minkowski Distance) & 70.8 & 75.0 & 66.7 \\
1-nn (Spearman Distance) & 77.1 & 54.2 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effectiveness of classifiers for modality integration with selection of features. Figure 8: Significance ranking for individual models, taking into account different feature usefulness evaluation methods.