## 2 Materials and Methods
### Annotation Procedure  
The TV Debates and Spanish Empathic VA datasets were labeled by emotion to achieve two useful and very valuable corpora to model emotions in Spanish. Emotions are traditionally represented by two models: a categorical representation, in which emotions consist of discrete labels, such as happiness, anger, etc. [47; 48], or an alternative approach that emphasizes the importance of the fundamental dimensions of valence and arousal in understanding emotional experience [49]. They are postulated as universal primitives in [49], and a feeling at any point on this two-dimensional space is called a core affect. A representation of the core affect is shown in Figure 1, where an emotion such as _sad_ is represented with a very low value of arousal and a neutral valence slightly shifted to the negative side. Other researchers have found a third dimension, dominance, to be important in representing emotional phenomena [50], particularly in social situations. In this work, we use both representations, the categorical one and the dimensional one. A set of categories of interest based on the selection provided in [51] was first considered. Then, the set was adapted to the specific features of each of the tasks presented above. For instance, _sad_ was not included in the TV Debates set, since sad emotions were not expected to appear in political debates. For the Empathic VA, a study was conducted to identify the emotions that were perceived by the users. In addition, the dimensional VAD model was also considered for both datasets. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Spanish**} \\ \hline User: & Para cenar suelo tomar o huevos con...o pescado con algo de tambien de verdura, y luego \\  & suelo tomar o tomo algunas frutas, suelo tener fruta. Y luego pues no tomo nada mas. \\ System: & Sueles merendar? \\ User: & A veecs st, depende...A veecs st meriendo y suelo merendar pues un...un cafe \\  & y algun dulce o algun bosadillito de jamón tambien suelo tomar. \\  & Depende de la ocasion, si estoy en casa o no. \\ System: & Entiendo, ¿Crees que comes bien? \\ User: & Creo que st. \\ System: & Dime [PERSON], ¿Hay algo que te gustaria cambiar en tu forma de comer? \\ User: & Pues nada porque como a gusto, no...no quiero otra cosa. \\ \hline \multicolumn{1}{c}{**English**} \\ \hline User: & For dinner, I usually have either eggs with...or fish with some vegetables too, and then \\  & I usually take or I take some fruits, I usually have fruit. And then, well, I do not take anything else. \\ System: & Do you usually have a snack? \\ User: & Sometimes, yes, it depends...Sometimes, yes, I take a snack; let us say a...a coffee \\  & and I also used to take some sweets or some small ham sandwich. \\  & It depends on the occasion, if I am at home or not. \\ System: & I understand. Do you think you eat well? \\ User: & I think so. \\ System: & Tell me [PERSON], Is there anything you would like to change in the way you eat? \\ User: & Well, nothing because I eat at ease; no,...I do not want anything else. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Small excerpt taken from the Empathic VA corpus. This sample is a segment of a conversation between a user and the WoZ. It shows that the WoZ generally asks questions and tries to obtain extensive answers from the user. The excerpt is shown in Spanish (original language) above and then in English (translation) below. The intrinsic subjectivity of the tasks makes obtaining a ground truth for the emotional status associated with an audio chunk difficult. One way to deal with this problem is to use the crowd truth [28], which is based on the intuition that human interpretation is subjective. Thus, measuring annotations on the same objects of interpretation across a crowd provides a useful representation of their subjectivity and the range of reasonable interpretations. In this work, crowd annotations was carried out through a crowdsourcing platform [29] to obtain emotional labels for both the VAD and categorical models.