## 3 Experiments
### Material Used, Initial Settings, and Conditions
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Speaker**} & \multirow{2}{*}{**F0\({}_{Mean}\) [Hz]**} & \multicolumn{6}{c}{**Number of Sentences/T\({}_{\text{DUR}}\) [s] (\(f_{\text{s}}\) = 16 kHz)**} \\ \cline{3-8}  & & **Orig** & **TTS\({}_{\text{A}}\)** & **TTS\({}_{\text{B}}\)** & **Synt1** & **Synt2** & **Synt3** \\ \hline M1 (AI) & 120 & 50/130 & 50/122 & 50/120 & 50/330 & 50/330 & 50/340 \\ M2 (JS) & 100 & 50/130 & 40/103 & 40/100 & 50/380 & 50/380 & 50/380 \\ F1 (KI) & 215 & 50/140 & 40/102 & 40/98 & 50/370 & 50/380 & 50/380 \\ F2 (SK) & 195 & 50/140 & 40/97 & 40/94 & 50/340 & 50/360 & 50/360 \\ \hline \end{tabular}
\end{table} 
Table 1: Description of the speech material used in both evaluation experiments. (FFT) points was set to \(N_{\text{FFT}}\) = 1024. A detailed list of the speech features used grouped by type is shown in Table 2.  
From these four types of features, four feature sets P0, P2, P4, and P42 were constructed for application in the GMM building part, as well as for classification in the main evaluation process. In correspondence with [10], all input feature vectors consisted of \(N_{\text{FEAT}}\) = 16 representative statistical parameters of speech features--see Table 3.  
The number of P-A classes was reduced to \(N_{\text{PC}}\) = 7 and \(N_{\text{AC}}\) = 5 so that the data of both tested databases were approximately evenly distributed. The similarity threshold \(D_{\text{THRESH}}\) for _FEO_ determination was empirically set to 5%. The values of importance weights together with the angles of the central and border definition points for functions \(IW_{\text{EQ1-4}}\) are shown in Table 4. Finally, the transformation curves were constructed using linear interpolation, as demonstrated graphically in Figure 7.  
In the GMM-based creation, training and classification process, a diagonal covariance matrix was selected due to its lower computational complexity. These program procedures were realized with the help of the \"Netlab\" pattern analysis toolbox [30] and the whole proposed automatic evaluation system was implemented in the Matlab computing system (ver. 2016b). The computational complexity was investigated using the UltraBook Lenovo \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Weighting**} & \multicolumn{3}{c}{**Importance Weights (\({}^{\text{A}}\))**} & \multicolumn{3}{c}{**Angle of Definition Points (\({}^{\text{B}}\))**} \\ \cline{2-7}  & _ntw1_ & _ntw0_ & _ntw2_ & _\#\text{START}_ & _\#\text{CENTR}_ & _\#\text{END}_ \\ \hline \(IW_{\text{EQ1}}\) & 0.75 & 1 & 0.75 & 0 & 45 & 90 \\ \(IW_{\text{EQ2}}\) & \(-\)0.75 & \(-\)1 & \(-\)0.75 & 90 & 135 & 180 \\ \(IW_{\text{EQ3}}\) & \(-\)0.75 & \(-\)0.5 & \(-\)0.5 & 180 & 225 & 270 \\ \(IW_{\text{EQ4}}\) & 0.5 & 0.5 & 0.75 & 270 & 315 & 360 \\ \hline \hline \end{tabular} \({}^{(\text{A})}\) Emotion quadrant importance weights corresponding to the angles. \({}^{\text{B}}\) Angles defined for the quadrant center (\(\phi_{\text{CENTR}}\Rightarrow\textit{ntw}_{0}\)) and the quadrant borders (\(\phi_{\text{START/END}}\Rightarrow\textit{ntw}_{1}/\textit{ntw}_{2}\)).  
\end{table} 
Table 4: Definition of central and border angles of definition points together with emotional quadrant importance weight coefficients for weighting functions \(IW_{\text{EQ1-4}}\).