Conflict intensity estimation from speech using Greedy forward-backward feature selection

In the recent years extracting non-trivial information from audio sources has become possible. The resulting data has induced a new area in speech technology known as computational paralinguistics. A task in this area was presented at the ComParE 2013 Challenge (using the SSPNet Conflict Corpus), where the task was to determine the intensity of conflicts arising in speech recordings, based only on the audio information. Most authors approached this task by following standard paralinguistic practice, where we extract a huge number of potential features and perform the actual classification or regression process in the hope that the machine learning method applied is able to completely ignore irrelevant features. Although current stateof-the-art methods can indeed handle an overcomplete feature set, studies show that they can still be aided by feature selection. We opted for a simple greedy feature selection algorithm, by which we were able to outperform all previous scores on the SSPNet Conflict dataset, achieving a UAR score of 85.6%.