Retrieving Categorical Emotions Using a Probabilistic Framework to Define Preference Learning Samples

Preference learning is an appealing approach for affective recognition. Instead of predicting the underlying emotional class of a sample, this framework relies on pairwise comparisons to rank-order the testing data according to an emotional dimension. This framework is relevant not only for continuous attributes such as arousal or valence, but also for categorical classes (e.g., is this sample happier than the other?). A preference learning system for categorical classes can have applications in several domains including retrieving emotional behaviors conveying a target emotion, and defining the emotional intensity associated with a given class. One important challenge to build such a system is to define relative labels defining the preference between training samples. Instead of building these labels from scratch, we propose a probabilistic framework that creates relative labels from existing categorical annotations. The approach considers individual assessments instead of consensus labels, creating a metrics that is sensitive to the underlying ambiguity of emotional classes. The proposed metric quantifies the likelihood that a sample belong to a target emotion. We build happy, angry and sad rank-classifiers using this metric. We evaluate the approach over cross-corpus experiments, showing improved performance over binary classifiers and rank-based classifiers trained with consensus labels.