@inproceedings{5126ab3964fc4037854ec88ac0c2f92d,
title = "Detecting vocal irony",
abstract = "We describe a data collection for vocal expression of ironic utterances and anger based on an Android app that was specifically developed for this study. The main aim of the investigation is to find evidence for a non-verbal expression of irony. A data set of 937 utterances was collected and labeled by six listeners for irony and anger. The automatically recognized textual content was labeled for sentiment. We report on experiments to classify ironic utterances based on sentiment and tone-of-voice. Baseline results show that an ironic voice can be detected automatically solely based on acoustic features in 69.3 UAR (unweighted average recall) and anger with 64.1 UAR. The performance drops by about 4% when it is calculated with a leave-one-speaker-out cross validation.",
author = "Felix Burkhardt and Benjamin Weiss and Florian Eyben and Jun Deng and Bj{\"o}rn Schuller",
note = "Publisher Copyright: {\textcopyright} 2018, The Author(s).; 27th International Conference on German Society for Computational Linguistics and Language Technology, GSCL 2017 ; Conference date: 13-09-2017 Through 14-09-2017",
year = "2018",
doi = "10.1007/978-3-319-73706-5_2",
language = "English",
isbn = "9783319737058",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "11--22",
editor = "Georg Rehm and Thierry Declerck",
booktitle = "Language Technologies for the Challenges of the Digital Age - 27th International Conference, GSCL 2017,Proceedings",
}