## 3 Analysis of Emotions and Classification Results
### Analysis of Emotions  
First, the categorical model annotation was analyzed. Table 5 shows the list of categories for each task along with the percentage of samples in each category in descending order. A minimum agreement of 0.6 (3/5) was requested to consider a sample to be valid, as mentioned above. Moreover, a minimum number of samples (1% of the total) was required in each class. These requirements led to a reduction in the valid samples, resulting in a set of 1266 samples for the TV Debates dataset and 4023 for the Empathic VA dataset when considering the categorical model. This table shows that different categories are predominant in each of the corpora. Some of them could be considered equivalent, such as calm/indifferent and calm/bored/tired, which are the most frequent categories in both sets. However, annoyed/tense, for instance, is the second most frequent class in the TV Debates dataset but was almost last in the Empathic VA dataset. In the same way, puzzled is almost absent (included in others) in the list for the TV Debates dataset. Table 5 also shows that both datasets are imbalanced, with the calm category being the majority class, with more than 70% of the samples. This reflects the spontaneous nature of the data, showing that, most of the time, people do not show extreme emotions. Moreover, more positive emotions, such as happy/amused, appear in the Empathic VA annotations and more negative emotions, such as annoyed/tense, appear in the TV Debates set. This difference comes from the specific nature of the tasks. During political debates (human-human interactions), people try to convince or even impose their opinions on other interlocutors. However, during coaching sessions (human-machine interaction), speakers are quiet and pay attention to the virtual agent while preparing their next exchange. As mentioned above, all of the samples were considered for the VAD model. Figure 6 shows the probability density function of each variable (valence, arousal, and dominance) that was obtained by a Gaussian kernel density estimator (upper row). Figure 6 also shows different 2D projections of the sample distribution in the 3D space (row below), representing each scenario in a different color. When regarding arousal, the Empathic VA dataset works in a very neutral scenario, where excitement is almost absent. In TV debates, although neutrality is also predominant, some excitement is perceived due to the nature of debates. The distribution of valence also shows a clear deviation towards positive values for the Empathic VA scenario, which is an indicator of the good acceptance of the system \begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{2}{c}{**TV Debates**} & \multicolumn{3}{c}{**Empathic VA**} \\ \hline
**Category** & **\% Audios** & **Category** & **\% Audios** \\ \hline calm/indifferent & 73.64 & calm/bored/tired & 79.47 \\ annoyed/tense & 14.32 & happy/amused & 13.55 \\ enthusiastic & 4.72 & puzzled & 3.11 \\ satisfied/pleased & 3.23 & annoyed/tense & 2.83 \\ worried & 2.12 & sad & 1.04 \\ interested. & 1.57 & & \\ others & 0.40 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Frequency of the different categories in the corpora. Both the TV Debates and Empathic VA datasets are unbalanced. The majority class is the neutral emotion (calm/indifferent and calm/bored/tired), with more than 70% of the samples. among users, whereas in TV debates, neutrality is predominant, with only a slight nuance towards positiveness. On the contrary, dominance is shifted towards dominant values in TV debates but remains neutral when users interact with the Empathic VA case. These results correlate well with the types of audio we deal with in the two scenarios. In the TV debates, people express themselves without becoming angry (low levels of excitement) but in a very assertive way (quite high dominance levels). Additionally, they appear to be neutral when communicating their opinions (valence tends to be neutral or slightly positive).