## 9 Experimental Assessment and Analysis
### Experiment 3: Basic Speech Unit Extraction  
Some examples of extraction of Basic Speech Units (BSU) are shown below. Suppose the human operator pronounes the training words / atfa /, / ala /. The training words are divided into the following Basic Speech Units: / atfa / = [-a] [atf] [tfa] [a-]; / ala / = [-a] [al] [la] [a-]. Therefore, the articulatory characteristics of the following six BSU are stored in the knowledge base: [-a], [a-], [atf], [tfa], [aal], [la].  
The values of the acoustic parameters \((AV,AF,F1,F2,F3,B1,B2,B3)\), described in terms of \([I(.),D(.),F(.)L_{1}(.),L_{2}(.)]\), obtained from the fuzzy/genetic algorithm for the BSU / atfa /, are shown in Table 5. The same table shows the acoustic parameters of the following BSUs: [-a], [atf], [tfa], [a-].  
The values of degrees of membership of the articulator parameters estimated by the algorithm for the same BSUs are reported in Table 6.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Nonsense Words** & **Words With Meaning** \\ \hline POSITIVE & 91\% NA & 83.16\% 84\% \\ NEGATIVE & 8.5\% NA & 16.84\% 16.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Speech/facial movements synchronization.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Nonsense Words**} & \multicolumn{3}{c}{**Words with Meaning**} \\ \hline  & **Only Audio** & **Audio + Video** & **Only Audio** & **Audio + Video** \\ \hline POSITIVE & 49.0\% 47.0\% & 61.5\% 77.08\% & 71.5\% 57.8\% & 80.0\% 79.96\% \\ NEGATIVE & 51.0\% 53.0\% & 38.5\% 29.7\% & 28.5\% 42.2 & 20.0\% 20.04\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Naturalness results of synthetic utterances for nonsense words and words with meaning.  
\begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline  & & **AV** & **AH** & **AF** & **F1** & **F2** & **F3** & **B1** & **B2** & **B3** \\ \hline
[-a] & D & & & & 65.00 & 65.00 & 65.00 & 65.00 & 65.00 \\  & L & 52.00 & & & 695.47 & 1197.87 & 2609.58 & 142.50 & 152.50 & 147.50 \\  & I & 65.00 & & & & & & & \\  & F & 130.00 & & & & & & & \\ \hline \([\mathrm{atf}]\) & D & & & & 45.00 & 45.00 & 45.00 & & & \\  & L & \(-\)4.67 & 40.00 & 246.89 & 1775.00 & 1892.50 & & & \\  & I & 25.00 & 65.00 & & & & & & \\  & F & & & 65.00 & 65.00 & 65.00 & & & \\ \hline \([\mathrm{tfa}]\) & D & & & 25.00 & 25.00 & 25.00 & 65.00 & 65.00 & 65.00 \\  & L & 52.00 & \(-\)4.67 & 695.47 & 1197.87 & 2609.58 & 142.50 & 152.50 & 147.50 \\  & I & & & 45.0 & 45.0 & 45.0 & & & \\ \hline
[a-] & D & & & 65.00 & 65.00 & 65.00 & 65.00 & 65.00 & 65.00 \\  & L & \(-\)4.67 & & 499.29 & 1541.19 & 2445.85 & 142.50 & 152.50 & 147.50 \\  & I & 65.00 & & 65.00 & 65.00 & 65.00 & 65.00 & 65.00 & 65.00 \\  & F & 65.00 & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Estimated acoustic parameters per transition.  
As a further example, in Figure 17 we provide three snapshots of the facial movements generated by the Italian word _/acia/_, (at]a /), generated artificially. To highlight the synchronization of the facial movements with the artificial word, the signal waveform is displayed in the panel above the facial movements.