Editing Affective computing (section)

====Databases====

The vast majority of present systems are data-dependent. This creates one of the biggest challenges in detecting emotions based on speech, as it implicates choosing an appropriate database used to train the classifier. Most of the currently possessed data was obtained from actors and is thus a representation of archetypal emotions. Those so-called acted databases are usually based on the Basic Emotions theory (by [[Paul Ekman]]), which assumes the existence of six basic emotions (anger, fear, disgust, surprise, joy, sadness), the others simply being a mix of the former ones.<ref name="Ekman, P. 1969">Ekman, P. & Friesen, W. V (1969). [http://www.communicationcache.com/uploads/1/0/8/8/10887248/the-repertoire-of-nonverbal-behavior-categories-origins-usage-and-coding.pdf The repertoire of nonverbal behavior: Categories, origins, usage, and coding]. Semiotica, 1, 49–98.</ref> Nevertheless, these still offer high audio quality and balanced classes (although often too few), which contribute to high success rates in recognizing emotions.

However, for real life application, naturalistic data is preferred. A naturalistic database can be produced by observation and analysis of subjects in their natural context. Ultimately, such database should allow the system to recognize emotions based on their context as well as work out the goals and outcomes of the interaction. The nature of this type of data allows for authentic real life implementation, due to the fact it describes states naturally occurring during the [[human–computer interaction]] (HCI).

Despite the numerous advantages which naturalistic data has over acted data, it is difficult to obtain and usually has low emotional intensity. Moreover, data obtained in a natural context has lower signal quality, due to surroundings noise and distance of the subjects from the microphone. The first attempt to produce such database was the FAU Aibo Emotion Corpus for CEICES (Combining Efforts for Improving Automatic Classification of Emotional User States), which was developed based on a realistic context of children (age 10–13) playing with Sony's Aibo robot pet.<ref name="Steidl-2011">{{cite web | last = Steidl | first = Stefan | title = FAU Aibo Emotion Corpus | publisher = Pattern Recognition Lab | date = 5 March 2011 | url = http://www5.cs.fau.de/de/mitarbeiter/steidl-stefan/fau-aibo-emotion-corpus/ }}</ref><ref name="Scherer-2010-p243">{{harvnb|Scherer|Bänziger|Roesch|2010|p=243}}</ref> Likewise, producing one standard database for all emotional research would provide a method of evaluating and comparing different affect recognition systems.