## 3 Analysis of Emotions and Classification Results
### Analysis of Emotions
In the Empathic VA scenario, the users are volunteers with a good predisposition and seem to be pleased with the system (positive valence values). They are relaxed while talking to the agent (levels of excitement tend toward neutrality) and are not intimidated (dominance values are around neutrality, with a slight shift to the right). The differences between human-human and human-machine interactions are also noticeable in the specific tasks we are dealing with. Human-human communication appears to be more intense and emotional, with higher arousal and dominance values. During communication with a machine, on the contrary, people are not confident and they tend to be expectant, which might be translated into low values of arousal/dominance and higher values of valence. The two models, categorical and VAD, were also considered together. Each category was represented in the 3D VAD space for comparison purposes. Specifically, the average of the valence, arousal, and dominance values of all of the audios labeled within a specific category was computed, and the resulting value was represented as a point in the 3D space. Figure 7 shows a 2D projection of the resulting representation. If we focus on the TV Debates dataset, we notice that interested and worried, the least representative categories, according to Table 5, are very close to the category with the highest number of samples, calm/indifferent, in all of the 2D projections (the purple, orange, and deep-blue points), so they were merged into only one category. The same happens with enthusiastic and satisfied (light-blue and green points). Overall, three different categories were finally considered for the TV Debates dataset. With regard to the Empathic VA scenario, puzzled and sad were merged into a single category because they are extremely close in all three projections, as shown in Figure 7. Thus, the final set of categories used for the classification experiments reported in this work are shown in Table 6.  
Figure 6: Representation of the VAD dimensional model. In the first row, each of the dimensions is displayed independently, letting us compare each corpus. The vertical lines are the cuts that have been used for the discretization (see Section 3.2). In the second row, a representation of the same dimensions but taken two-by-two is displayed, helping to provide a better understanding of the corpora. The distribution of categories is quite different for both sets due to the nature of the scenarios (see Table 6). For instance, annoyed/tense, although present in both tasks, has a very different significance in the TV Debates dataset (almost 15%) and in the Empathic VA dataset (less than 3%). Puzzled was not considered in the TV Debates dataset due to the low number of samples labeled with that emotion, but it entails 3% of the samples in the Empathic VA dataset. Moreover, the final category, puzzled + sad, represented by the union of brown and purple points (low levels of valence and dominance) is not represented in the TV Debates dataset and is slightly separated from the other categories in the Empathic VA scenario. Moreover, Figure 7 shows that the location of annoyed/tense (red point, which is in fact quite separated from the other categories) is closer to neutrality in the Empathic VA scenario (lower excitement levels and lower negative values of valence) than in the TV Debates dataset, meaning that this negative feeling is softer when interacting with the machine and within this specific scenario. This correlates well with the idea that people interacting with the Empathic VA scenario are not angry with the system, and if they experience any anger, their feelings are more related to annoyance, which is quite common during debates. Furthermore, speakers in debates do not usually show that they are in an unexpected situation (puzzled), since this emotion can be interpreted as weakness, while it is often shown in interactions with machines. In fact, puzzled was detected in the Empathic VA scenario.