@article{65c4eb07a778483fb1adde8f5d6884ac,
title = "Whodunnit - Searching for the most important feature types signalling emotion-related user states in speech",
abstract = "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.",
keywords = "Automatic classification, Emotion, Feature selection, Feature types",
author = "Anton Batliner and Stefan Steidl and Bj{\"o}rn Schuller and Dino Seppi and Thurid Vogt and Johannes Wagner and Laurence Devillers and Laurence Vidrascu and Vered Aharonson and Loic Kessous and Noam Amir",
note = "Funding Information: The initiative to co-operate was taken within the European Network of Excellence (NoE) HUMAINE under the name Combining Efforts for Improving automatic Classification of Emotional user States (CEICES). This work was partly funded by the EU in the projects PF-STAR under Grant IST-2001-37599 and HUMAINE under Grant IST-2002-50742 . The responsibility lies with the authors. ",
year = "2011",
month = jan,
doi = "10.1016/j.csl.2009.12.003",
language = "אנגלית",
volume = "25",
pages = "4--28",
journal = "Computer Speech and Language",
issn = "0885-2308",
publisher = "Academic Press",
number = "1",
}