## 3 Experiments
### Experiments Performed and the Results Obtained  
Experiments in this research were realized in two steps. An auxiliary analysis had to be performed before the main evaluation. The first part of the preliminary investigations was motivated by seeking an appropriate setting of control parameters for the GMM-based classification process. The positions of the originals in the P-A space were analyzed statistically using the class centers [\(C_{\text{PO}}\), \(C_{\text{AO}}\)] and their dispersions represented by the std values \(std_{\text{PO}}\), \(std_{\text{AO}}\). As the originals were the same for both testing speech corpora SC1 and SC2, the results obtained are applicable in all our next evaluation experiments. The second part focused on the functionality testing of the whole evaluation process. These investigations were performed using the speech corpus SC2 and three types of synthesis methods (\(Synt1\), \(Synt2\), and \(Synt3\)). The first part of the auxiliary experiments consists of the following three investigations areas:  
1. Comparison of computational complexity expressed by CPU times of GMM creation and training and CPU times of GMM 2D classification of originals in the P-A space for \(N_{\text{MIX}}\) = [8, 16, 32, 64, 128, 256, and 512] and for both databases (DB\({}_{1}\) and DB\({}_{2}\)); obtained results are presented numerically in Tables 5 and 6. 2. Mapping of the effect of the number of Gaussian mixtures on the obtained \(std_{\text{PO}}\) and \(std_{\text{AO}}\) values of originals--see the summary comparison for both databases with the voices M1 and F1, using the feature set P4 in Figure 8.
3. Analysis of the influence of different types of speech features in the input vector on \(std_{\text{PO}}\) and \(std_{\text{AO}}\) values for the feature sets P0, P2, P4, and P42, using both databases and \(N_{\text{MIX}}\) = 128--see the box-plot of basic statistical parameters and \(C_{\text{PO}}\), \(C_{\text{AO}}\) positions for all four voices in Figure 9. The visualization of the center positions and their dispersions in the P-A scale for all four voices, using both databases DB\({}_{1}\) and DB\({}_{2}\), \(N_{\text{MIX}}\) = 128, and the feature set P4 is shown in Figure 10.
4. In the second part of the preliminary investigations, we tested the setting of other parameters with a possible influence on the stability of the partial results and the final decision of the main evaluation experiments. We analyzed and compared several values obtained from the sum vectors: magnitudes and angles, SDPs after weighting in agreement with the localized emotion quadrants, order differences \(Dso\), and final decisions FEO. For these values, we analyzed the influence of: (a) The type of the database (DB\({}_{1}\)/DB\({}_{2}\)) for training of the GMMs in the case of comparison of two methods of prosody manipulation in the TTS system (TTS\({}_{1}\)/TTS\({}_{2}\))--see the numerical comparison of partial evaluation parameters as well as the FEO decisions using \(N_{\text{MIX}}\) = 128, and the feature set P4 for the M1 voice in Table 7, and for the F1 voice in Table 8.  
Figure 7: Visualization of importance weighting functions for the final normalized sum vector localization inside emotion quadrants EQ\({}_{1-4}\) (**a–d**). The used number \(N_{\text{TS}}=\{10,25,40,\) and \(50\}\) of tested synthetic sentences in the case of comparison of three synthesis methods (\(Synt1/Synt2/Synt3\))--compare the obtained values in Table 9 for the M1 voice, \(N_{\text{MIX}}=128\), and the feature set P4. Different number of tested sentences was applied in the Synt3 type, sentence sets for Synt1 and Synt2 were complete (\(N_{\text{TS}}=50\)).