Whodunnit - Searching for the Most Important Feature Types Signalling Emotion-Related User States in Speech

Anton Batliner , Stefan Steidl , Björn Schuller (1) , Dino Seppi , Thurid Vogt , Johannes Wagner , Laurence Devillers (2) , Laurence Vidrascu (2) , Vered Aharonson (3) , Loic Kessous (4) , Noam Amir (4)

1 TUM - Technische Universität Munchen - Technical University Munich - Université Technique de Munich
2 Spoken Language Processing Group
3 AFEKA - The Academic College of Engineering in Tel Aviv
4 Sackler Faculty of Medicine

In this article, we describe and interpret a set of acoustic and linguistic features that characterise emotional/emotion-related user states - confined 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 different 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 different feature and extraction types. We establish different measures of impact and discuss the mutual influence of acoustics and linguistics.

Mots clés: feature types; feature selection; automatic classification; emotion

Anton Batliner, Stefan Steidl, Björn Schuller, Dino Seppi, Thurid Vogt, et al.. Whodunnit - Searching for the Most Important Feature Types Signalling Emotion-Related User States in Speech. Computer Speech and Language, 2010, 25 (1), pp.4. ⟨10.1016/j.csl.2009.12.003⟩. ⟨hal-00661911⟩