Fiction support for realistic portrayals of fear-type emotional manifestations
The present paper aims at filling the lack that currently exists with respect to databases containing emotional manifestations. Emotions, such as strong emotions, are indeed difficult to collect in real-life. They occur during contexts, which are generally unpredictable, and some of them such as anger are less frequent in public life than in private. Even though such emotions are not so present in existing databases, the need for applications, which target them (crisis management, surveillance, strategic intelligence, etc.), and the need for emotional recordings is even more acute. We propose here to use fictional media to compensate for the difficulty of collecting strong emotions. Emotions in realistic fictions are portrayed by skilled actors in interpersonal interactions. The mise-en-scene of the actors tends to stir genuine emotions. In addition, fiction offers an overall view of emotional manifestations in various real-life contexts: face-to-face interactions, phone calls, interviews, emotional event reporting vs. in situ emotional manifestations. A fear-type emotion recognition system has been developed, that is based on acoustic models learnt from the fiction corpus. This paper aims at providing an in-depth analysis of the various factors that may influence the system behaviour: the annotation issue and the acoustic features behaviour. These two aspects emphasize the main feature of fiction: the variety of the emotional manifestations and of their context.
fiction corpus affective speech multi-level annotation scheme acostic features unvoiced speech