A Quick Sequential Forward Floating Feature Selection Algorithm for Emotion Detection from Speech

In this paper we present an improved Sequential Forward Floating Search algorithm. Subsequently, extensive tests are carried out on a selection of French emotional language resources well suited for a first impression on general applicability. A detailed analysis is presented to test the various modifications suggested one-by-one. Our conclusion is that the modification in the forward step result in a considerable improvement in speed (similar to 80%) while no considerable and systematic loss in quality is experienced. The modifications in the backward step seem to have only significance when a higher number of features is achieved. The final clarification of this issue remains the task of future work. As a result we may suggest a quick feature selection algorithm, which is practically more suitable for the state of the art, larger corpora and wider feature-banks. Our quick SFFS is general: it can also be used in any other field of application.