Title
Aggression detection in speech using sensor and semantic information
Author
Lefter, I.
Rothkrantz, L.J.M.
Burghouts, G.J.
Publication year
2012
Abstract
By analyzing a multimodal (audio-visual) database with aggressive incidents in trains, we have observed that there are no trivial fusion algorithms to successfully predict multimodal aggression based on unimodal sensor inputs. We proposed a fusion framework that contains a set of intermediate level variables (meta-features) between the low level sensor features and the multimodal aggression detection [1]. In this paper we predict the multimodal level of aggression and two of the meta-features: Context and Semantics. We do this based on the audio stream, from which we extract both acoustic (nonverbal) and linguistic (verbal) information. Given the spontaneous nature of speech in the database, we rely on a keyword spotting approach in the case of verbal information. We have found the existence of 6 semantic groups of keywords that have a positive influence on the prediction of aggression and of the two meta-features. © 2012 Springer-Verlag.
Subject
Aggression detection
Emotional words spotting
Multimodal fusion
Safety and Security
Defence, Safety and Security
Physics & Electronics
II - Intelligent Imaging
TS - Technical Sciences
To reference this document use:
http://resolver.tudelft.nl/uuid:24cd8426-9035-4ff1-b767-9b1dea3ff88a
DOI
https://doi.org/10.1007/978-3-642-32790-2_81
TNO identifier
463900
Publisher
Springer, Berlin : [etc]
Source
15th International Conference on Text, Speech and Dialogue, TSD 2012, 3-7 September 2012, Brno, Czech Republic, 665-672
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Document type
conference paper