Projecting emotional speech into arousal-valence space using pairwise preference learning

Emotion recognition in speech is a very challenging task in the speech processing domain. Because of the continuity characteristics of human emotion, most of the recent research focuses on recognising emotion in a continuous space. While previous attempts for speech emotion annotation adopted the likert-like scaling system in a continuous space and relied on prediction models to predict emotion we, in this research, propose a new method for data labelling based on a pairwise data annotation. A set of constraints was proposed to decrease the number of pairs required to label. The annotated data is used to construct a regression model using the pairwise evolutionary multivariate adaptive regression spline method. The experiments performed show high recognition accuracies compared to the baseline random pairwise assignment.