Energy and F0 contour modeling with functional data analysis for emotional speech detection

This paper proposes the use of reference models to detect emotional prominence in the energy and F0 contours. The proposed framework aims to model the intrinsic variability of these prosodic features. We present a novel approach based on Functional Data Analysis (FDA) to build reference models using a family of energy and F0 contours, which are implemented with lexicon-independent models. The neutral models are represented by bases of functions and the testing energy and F0 contours are characterized by their projections onto the corresponding bases. The proposed system can lead to accuracies as high as 80.4% in binary emotion classification in the EMODB corpus, which is 17.6% higher than the one achieved by a benchmark classifier trained with sentence level prosodic features. The approach is also evaluated with the SEMAINE corpus, showing that it can be effectively used in real applications.