Paper Info
Title
Private emotions versus social interaction: a data-driven approach towards analysing emotion in speech
Abstract
The `traditional' first two dimensions in emotion research are VALENCE and AROUSAL. Normally, they are obtained by using elicited, acted data. In this paper, we use realistic, spontaneous speech data from our `AIBO' corpus (human-robot communication, children interacting with Sony's AIBO robot). The recordings were done in a Wizard-of-Oz scenario: the children believed that AIBO obeys their commands; in fact, AIBO followed a fixed script and often disobeyed. Five labellers annotated each word as belonging to one of eleven emotion-related states; seven of these states which occurred frequently enough are dealt with in this paper. The confusion matrices of these labels were used in a Non-Metrical Multi-dimensional Scaling to display two dimensions; the first we interpret as VALENCE, the second, however, not as AROUSAL but as INTERACTION, i.e., addressing oneself (angry, joyful) or the communication partner (motherese, reprimanding). We show that it depends on the specifity of the scenario and on the subjects' conceptualizations whether this new dimension can be observed, and discuss impacts on the practice of labelling and processing emotional data. Two-dimensional solutions based on acoustic and linguistic features that were used for automatic classification of these emotional states are interpreted along the same lines.
Year
DOI
Venue
2008
10.1007/s11257-007-9039-4
User Model. User-Adapt. Interact.
Keywords
Field
DocType
Emotion,Speech,Dimensions,Categories,Annotation,Data-driven,Non-metrical multi-dimensional scaling
Social relation,Arousal,Confusion,Data-driven,Computer science,AIBO,Artificial intelligence,Machine learning
Journal
Volume
Issue
ISSN
18
1-2
0924-1868
Citations 
PageRank 
References 
42
2.27
18
Authors
4
Name
Order
Citations
PageRank
Anton Batliner11502131.55
Stefan Steidl2114079.71
Christian Hacker323522.51
Elmar Nöth4959158.94