## 4 The Audio-Based Emotion Detection Component: GEVA
### Classifier  
Once the voice features have been extracted, it is necessary to classify them into emotions, that is, to decide which emotion corresponds to which values. Next, three possible solutions to train the classifier are shown. * **Offline**: this approximation consists in training the automatic emotion detection system before its use in the RDS. There are, at least, two possibilities: 1. A unique universal classifier (for all types of users) previously trained using examples of sentences from a great variety of users (different ages, languages and gender). Nevertheless, in the literature, there are some works that state that using a classifier for each user is more precise than using a universal one [53; 63]. 2. A classifier for each possible language: It is assumed that this option would obtain greater success rates. The RDS would inform the emotion detection module about the language of the user. Another variation would be the use of two different classifiers for each language: one for masculine and one for feminine voices. * **Online**: This option implies building the classifier during real interactions with the user using the RDS. During the registration phase (which is carried out only once per user) the system learns the characteristics of the user, such as his name, language, voice tone, the way he expresses his emotions, _etc._ Two possibilities are considered to build the classifier during the registration phase:1. The user registers in the system, and an external supervisor tags each locution with the perceived emotion. This approach presents several disadvantages: first, the user, during the registration phase, will probably not express the four selected emotions; second, although the user expresses all of them, he/she will not do it many times, so only a very small amount of examples would be obtained for each emotion; and third, we need a supervisor to tag such emotions. 2. During the registration phase, the system asks the user to express each emotion it needs to learn. Therefore, the user must simulate these emotions multiple times to facilitate the learning of features, and the system builds the classifier. In this case, the dialog is the supervisor, not a person. The disadvantages are related to the fact that the faked emotions are not always so similar to the natural ones. Besides, this registration process should be similar to the first moment of meeting among humans, and \"faking\" emotions does not seem to be very natural. * **Mixed**: in this approximation, a classifier is built based on the relative relationships among the extracted features instead of their absolute values (e.g., happiness is related to a pitch 20% higher than the neutral emotion). The construction of this classifier would be _offline_. Later, during the registration phase, in real (_online_) interactions, the statistics values associated with the neutral tone of the user are obtained. Using these values, the classifier is scaled offline, such that these relative relationships among features are converted into rules (as absolute numeric values). This approach requires the assumption of the idea that the user emotion, during the registration phase, is neutral (this approach is proposed in [63]). The main disadvantage of this approximation is that it will not work properly if the user registers in the system using a non-neutral tone voice. In this work, the selected option is the building of a universal emotion classifier trained _offline_. For this option, as previously explained, a set of tagged locutions for each emotion (happiness, sadness, surprise and neutral) is needed. The following sources, which provide voice samples related to several emotions in different languages, ages and gender, are used:  
* Voice examples from the developers simulating emotions. * Interviews with colleagues asked to fake emotions. * Real or spontaneous interactions between the robot and colleagues. * Interviews obtained from the Internet. * TV shows from the Internet. * Audiobooks from the Internet.