## 3 Experiments
### Experiments Performed and the Results Obtained
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Type of Originals (A)**} & \multicolumn{6}{c}{**CPU Time [minsec]—Total (Mean for Each of Sentence of Originals)**} \\ \cline{2-9}  & \(N_{\text{MIX}}\) = 8 & \(N_{\text{MIX}}\) = 16 & \(N_{\text{MIX}}\) = 32 & \(N_{\text{MIX}}\) = 64 & \(N_{\text{MIX}}\) = 128 & \(N_{\text{MIX}}\) = 526 & \(N_{\text{MIX}}\) = 512 \\ \hline male M1 (DB\({}_{1}\)) & 8.5 (0.2) & 14 (0.3) & 23 (0.5) & 44 (0.9) & 1:33 (1.7) & 2:35 (3.1) & 4:19 (5.2) \\ female F1 (DB\({}_{1}\)) & 8.8 (0.2) & 14 (0.3) & 24 (0.5) & 46 (0.9) & 1:25 (1.7) & 2:38 (3.2) & 4:22 (5.3) \\ male M1 (DB\({}_{2}\)) & 8.7 (0.2) & 13 (0.3) & 23 (0.5) & 45 (0.9) & 1:23 (1.7) & 2:36 (3.1) & 4:16 (5.1) \\ female F1 (DB\({}_{2}\)) & 8.7 (0.2) & 14 (0.3) & 24 (0.5) & 47 (0.9) & 1.23 (1.7) & 2:39 (3.2) & 4:22 (5.2) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of CPU times for GMM 2D classification of originals in the P-A space using different number of mixtures \(N_{\text{MIX}}\), both databases (DB\({}_{1}\)/DB\({}_{2}\)) for M1 and F1 voices. Figure 8: Distribution of centers of originals using different number of mixtures \(N_{\text{MIX}}=\{8,16,32,64,128,256,512\}\): (**a**) \(std_{\text{PO}}\) values, (**b**) \(std_{\text{AO}}\) values; for M1 and F1 voices; used DB\({}_{1}\) (upper graphs) and DB\({}_{2}\) (lower graphs), feature set P4. The main evaluation consists of a summary comparison between the objective results by the proposed system and the subjective results achieved using the standard listening test method. In these final experiments, the sentences of the synthetic speech extracted from both corpora SC1 and SC2 and all four voices were tested, while the original sentences from speakers were the same for both corpora. In the case of the sentences from the SC1, the GMM-based results were compared with the subjective results by a large three-scale preference listening test. This test compared two versions of the same utterance synthesized by \(TTS_{A}\) and \(TTS_{B}\) prosody generation methods. The listeners had to choose whether _\"A sounds better\"_, _\"A sounds similar to B\"_, or _\"B sounds better\"_. The evaluation set was formed by 25 pairs of randomly selected synthetic sentences for each of four synthetic voices, so 100 sentences were compared in total. Twenty-two evaluators (of which seven were speech synthesis experts, six were phoneticians and nine were naive listeners) participated in this subjective listening test experiment. The evaluation carried out is described in more detail in [11]. The final results of the automatic evaluation system based on the GMM classification in the P-A space are compared visually with the evaluation results of the standard listening tests in the bar-graphs in Figure 11.  
In the second subjective evaluation (the MUSHRA listening test), multiple audio stimuli were used for the comparison of the synthesis tested with a high quality reference signal and impaired anchor signals resembling the system's artifacts. Both the reference and the anchor signals were hidden from the listener. The subjective audio quality of the speech recordings was scored according to the continuous quality scale with the range from 0 (poor) to 100 (excellent). For each of the four speakers and each of the 10 sets of utterances, there were four sentences to be scored by the listener. One of them was uttered in high-quality original speech Orig and the three remaining ones were synthesized by the