Whodunnit - Searching for the Most Important Feature Types Signalling Emotion-Related User States in Speech - Batliner, Anton; Steidl, Stefan; Schuller, Björn; Seppi, Dino; Vogt, Thurid; Wagner, Johannes; Devillers, Laurence; Vidrascu, Laurence; Aharonson, Vered; Kessous, Loic; Amir, Noam (Computer Speech and Language, 2011)
In this article, we describe and interpret a set of acoustic and linguistic features that characterise emotional/emotion-related user states con ned to the one database processed: four classes in a German corpus of children interacting with a pet robot. To this end, we collected a very large feature vector consisting of more than 4000 features extracted at di erent sites. We performed extensive feature selection (Sequential Forward Floating Search) for seven acoustic and four linguistic types of features, ending up in a small number of `most important' features which we try to interpret by discussing the impact of di erent feature and extraction types. We establish di erent measures of impact and discuss the mutual in uence of acoustics and linguistics.
Keywords: feature types; feature selection; automatic classification; emotion