Brute-forcing hierarchical functionals for paralinguistics: A waste of feature space?

While the "'quasi-state-of-the-art"' towards acoustic emotion recognition relies on multivariate time-series analysis of e.g. pitch, energy, or MFCC by statistical functionals as moments or extrema, only few respect statistical noise by outliers due to too long segments as turns. Such noise can be overcome by hierarchical functionals as means of extrema over smaller units as words or chunks. Segmentation of such units however usually relies on transcription. We therefore discuss hierarchical functionals based on automatic segmentation and their systematic generation as opposed to common expert-driven selection. To cope with rapidly growing feature spaces ¿5k, we discuss data-driven two-stage compression based on SVM-SFFS. Extensive test-runs are carried out on two known emotion and behavior corpora, and show superiority of the suggested approach.