Real-Life Emotion Recognition in Speech

Authors:
Laurence Devillers;Laurence Vidrascu
Affiliations:
LIMSI-CNRS, BP133, 91403 Orsay Cedex,;LIMSI-CNRS, BP133, 91403 Orsay Cedex,
Venue:
Speaker Classification II
Year:
2007

Citing 6
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Abstract

This article is dedicated to Real-life emotion detection using a corpus of real agent-client spoken dialogs from a medical emergency call center. Emotion annotations have been done by two experts with twenty verbal classes organized in eight macro-classes. Two studies are reported in this paper with the four macro classes: Relief, Anger, Fear and Sadness: the first investigates automatic emotion detection using linguistic information whith a detection score of about 78% and a very good detection of Relief, whereas the second investigates emotion detection with paralinguistic cues with 60% of good detection, Fear being best detected.