## 4 Discussion of the Obtained Results  
The detailed comparison of computational complexity demonstrates a great increase in CPU time for GMM creation and training using higher number of mixtures \(N_{\text{MIX}}\). To obtain GMMs for seven Pleasure or five Arousal classes using the sound database (IADS-2), the necessary CPU time was 43 s for eight mixture components and about 1890 s for \(N_{\text{MIX}}=512\) (see first two rows in Table 5) representing a 44-fold increase. With the speech database (MSP-IMPROV), separate models for male and female voices were created, hence the differences in the CPU times are halved: about 19 s for \(N_{\text{MIX}}=8\) and 900 s for the maximum of 512 mixtures, (approx. 47-fold increase). The situation is similar for both voices--male and female ones. For 2D GMM classification of original sentences of real speakers (a set of 50 in total) with these models, the CPU times are about 7 times lower, however, 250 s for the maximum \(N_{\text{MIX}}=512\) is still too high--beyond the possibility of real-time processing. For the results obtained in the classification phase, the CPU times are affected neither by the voice (male/female) nor by the database (DB\({}_{1}\)/DB\({}_{2}\)), as documented in Table 6.  
Figure 11: Final evaluation results of two types of the prosody manipulation in the TTS system using: **(a)** the GMM-based method and DB\({}_{1}\), **(b)** the GMM-based method and DB\({}_{2}\), **(c)** the listening test approach for all four tested voices; for results of the GMM method the feature set P4 and \(N_{\text{MIX}}=128\) was applied. Figure 12: Summary comparison: **(a)** FEO decisions by the GMM-based evaluation, **(b)** using DB\({}_{2}\) and \(N_{\text{MIX}}=128\), **(c)** results by the MUSHRA listening test for all four evaluated voices; FEO: \"1\" = the best, \"2\" = medium, \"3\" = the worst; “1/2”, “2/3” = similar. The analysis of the effect of the number of Gaussian mixtures on the obtained dispersion of the originals' centers expressed by the \(std_{\text{PO}}\) and \(std_{\text{AO}}\) values has shown their monotonous decrease--see the graphs in Figure 8. The falling trend is the same for the male (M1) as well as the female (F1) voices, greater differences are observed for the DB\({}_{2}\) used. For maximum accuracy of the evaluation results, low \(std_{\text{PO}}\) and \(std_{\text{AO}}\) values are necessary. It is practically fulfilled for the sound database in the case of \(N_{\text{MIX}}=128\) and for the DB\({}_{2}\) using \(N_{\text{MIX}}=512\). With respect to the CPU times, we have finally chosen \(N_{\text{MIX}}=128\) to be used as a compromise value in further experiments (with CPU times for GMM classification being about 0.5 s per one sentence tested). The next auxiliary analysis of dispersion of the originals around the centers dealt with different feature sets used for GMM classification. As can be seen in a box-plot comparison in Figure 9, lower mean values of \(std_{\text{PO}}\) and \(std_{\text{AO}}\) parameters are achieved with P0 and P4 sets for both databases (DB\({}_{1}\), DB\({}_{2}\)). Considering the structure of the feature sets in Table 3, we finally decided to use the set P4 with a more balanced distribution of speech features (prosodic, spectral, and temporal types). For practical testing of the functionality of the evaluation system we calculated and compared partial results comprising centers of originals, \(M_{\text{FV}}\) and \(\phi_{\text{FV}}\) of sum vectors, summary distribution parameters, differences \(Dso_{\text{Y-Y}}\) and FEO decisions for M1 and F1 voices depending on the sound/speech database used (see Tables 7 and 8). The \(M_{\text{FV}}\) parameters in the second columns of both tables show similar values for both types of prosody manipulation. For better discrimination between them, the emotional quadrant importance weights are applied. In principle, it increases the complexity of the whole evaluation algorithm.