## 4 Discussion  
### Analysis of Emotions  
The perceived emotions provide very valuable information to profile the specific features of the speakers in a scenario. The analyses carried out showed, for instance, \begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**TV Debates**} & \multicolumn{6}{c}{**Empathic VA**} \\ \cline{2-10}  & & **Acc** & **P/P\({}_{W}\)** & **R/R\({}_{W}\)** & **F1/F1\({}_{W}\)** & **Acc** & **P/P\({}_{W}\)** & **R/R\({}_{W}\)** & **F1/F1\({}_{W}\)** & \\ \hline \multirow{6}{*}{Cat.} & overall & 0.65 & 0.56/0.65 & 0.57/0.65 & 0.56/0.65 & 0.73 & 0.34/0.64 & 0.27/0.73 & 0.26/0.66 & overall \\ \cline{2-10}  & CALM & & 0.75 & 0.74 & 0.75 & & 0.76 & 0.94 & 0.84 & CALM \\  & ENT & & 0.42 & 0.45 & 0.43 & & 0.26 & 0.08 & 0.13 & HAPPY \\  & ANN & & 0.51 & 0.50 & 0.51 & & 0.20 & 0.01 & 0.03 & ANN \\  & & & & & & & 0.14 & 0.03 & 0.05 & PUZZ \\ \hline \multirow{3}{*}{Aro.} & overall & 0.76 & 0.69/0.77 & 0.71/0.76 & 0.70/0.77 & 0.58 & 0.58/0.58 & 0.56/0.58 & 0.54/0.55 & overall \\  & neutral & & 0.86 & 0.82 & 0.84 & & 0.59 & 0.81 & 0.68 & neutral \\  & excited & & 0.53 & 0.60 & 0.56 & & 0.57 & 0.30 & 0.40 & excited \\ \hline \multirow{6}{*}{Val.} & overall & 0.51 & 0.48/0.52 & 0.47/0.51 & 0.47/0.52 & 0.55 & 0.41/0.52 & 0.39/0.55 & 0.38/0.52 & overall \\  & negative & & 0.41 & 0.36 & 0.38 & & 0.21 & 0.21 & 0.21 & negative \\  & neutral & & 0.60 & 0.59 & 0.59 & & 0.63 & 0.77 & 0.69 & neutral \\  & positive & & 0.42 & 0.47 & 0.44 & & 0.40 & 0.18 & 0.25 & positive \\ \hline \multirow{2}{*}{Dom.} & overall & 0.63 & 0.58/0.69 & 0.60/0.63 & 0.58/0.65 & 0.63 & 0.59/0.62 & 0.58/0.63 & 0.59/0.63 & overall \\  & neutral & & 0.80 & 0.66 & 0.72 & & 0.71 & 0.74 & 0.73 & neutral \\  & dominant & & 0.36 & 0.54 & 0.43 & & 0.47 & 0.42 & 0.45 & dominant \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Classification results on the spectrogram input for each of the problems (categorical and each dimension of the VAD) and corpus (TV Debates and Empathic VA). Each problem has a row per category with the precision, recall, and \(F1\)-score metrics and a row of means (Overall) that shows the macro and weighted average measures of the metrics for all categories. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**TV Debates**} & \multicolumn{6}{c}{**Empathic VA**} \\ \cline{2-10}  & **Acc** & **P/P\({}_{W}\)** & **R/R\({}_{W}\)** & **F1/F1\({}_{W}\)** & **Acc** & **P/P\({}_{W}\)** & **R/R\({}_{W}\)** & **F1/F1\({}_{W}\)** \\ \hline categorical & 0.63 & 0.44/0.53 & 0.34/0.63 & 0.27/0.50 & 0.79 & 0.20/0.63 & 0.25/0.79 & 0.22/0.70 \\ \hline arousal & 0.75 & 0.64/0.71 & 0.56/0.75 & 0.55/0.70 & 0.53 & 0.51/0.51 & 0.51/0.53 & 0.49/0.51 \\ \hline valence & 0.38 & 0.29/0.39 & 0.35/0.38 & 0.27/0.33 & 0.62 & 0.33/0.48 & 0.33/0.62 & 0.26/0.48 \\ \hline dominance & 0.74 & 0.37/0.54 & 0.50/0.74 & 0.42/0.63 & 0.63 & 0.55/0.59 & 0.53/0.63 & 0.52/0.59 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classification results on wav2 vec input for each of the problems (categorical and each dimension of the VAD) and corpus (TV Debates and Empathic VA). Each problem shows the accuracy, and macro and weighted average measures of the metrics (precision, recall, and \(F1\)-score) for all categories. the predominant neutrality in scenarios where spontaneous speech was considered. As mentioned above, most of the time, conversational speech does not show extreme emotions and tends to be calm. However, some differences are found when analyzing different scenarios such as the ones tackled in this work. Quite noticeable was that human-human interactions in a TV Debate format showed higher levels of excitement and dominance and more negativeness, whereas human-machine interactions in the Empathic VA task showed more positive feelings and lower values of excitement and dominance. These observations could be easily reflected using the VAD model. However, when trying to translate it to categories, finding appropriate ones is difficult without a previous perception study.