PROCEEDINGS IPMU '08
Real-life emotions detection on Human-Human spoken dialogs
L. Devillers, L. Vidrascu
In the paper we present emotional
annotation for a corpus of naturalistic
data recorded in a French Medical call
center. When studying real-life data,
there are few occurrences of full blown
emotions but also there are many
emotion mixtures. To represent emotion
mixtures, an annotation scheme with
the possibility to choose two verbal
labels per segment was used by 2 expert
annotators. A closer study of these
mixtures has been carried out, revealing
the presence of conflictual valence
emotions. Results of the perceptive test
show 85% of consensus between expert
and naive labellers. When selecting the
non-complex part of the annotated
corpus, the performances obtained are
around 60% of good detection between
four emotions for respectively agents
and callers.
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