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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Devillers, L., Vidrascu, L., Lamel, L.: Challenges in real-life emotion annotation and machine learning based detection, Journal of Neural Networks 2005. special issue: Emotion and Brain 18(4), 407–422 (2005)
Devillers, L., Abrilian, S., Martin, J.-C.: Representing real life emotions in audiovisual data with non basic emotional patterns and context features. In: Tao, J., Tan, T., Picard, R.W. (eds.) ACII 2005. LNCS, vol. 3784, Springer, Heidelberg (2005)
Vidrascu, L., Devillers, L.: Real-life Emotions Representation and Detection in Call Centers. In: Tao, J., Tan, T., Picard, R.W. (eds.) ACII 2005. LNCS, vol. 3784, Springer, Heidelberg (2005)
Clavel, C., Vasilescu, I., Devillers, L., Ehrette, T.: Fiction database for emotion detection in abnormal situation. In: ICSLP (2004)
Cowie, R., Cornelius, R.R: Describing the emotional states expressed in speech. Speech Communication 40(1-2), 5–32 (2003)
Campbell, N.: Accounting for Voice Quality Variation, Speech Prosody, 217–220 (2004)
Batliner, A., Fisher, K., Huber, R., Spilker, J., Noth, E.: How to Find Trouble in Communication. Journal of Speech Communication 40, 117–143 (2003)
Lee, C.M., Narayanan, S., Pieraccini, R.: Combining acoustic and language information for emotion recognition. In: ICSLP (2002)
Forbes-Riley, K., Litman, D.: Predicting Emotion in Spoken Dialogue from Multiple Knowledge Sources. In: Proceedings of HLT/NAACL (2004)
Devillers, L., Vasilescu, I., Lamel, L.: Emotion detection in task-oriented dialog corpus. In: Proceedings of IEEE International Conference on Multimedia (2003)
Devillers, L., Vasilescu, I., Vidrascu, L.: Anger versus Fear detection in recorded conversations. In: Proceedings of Speech Prosody, pp. 205–208 (2004)
Steidl, S., Levit, M., Batliner, A., Nth, E., Niemann, E.: Off all things the measure is man Automatic classification of emotions and inter-labeler consistency. In: Proceeding of the IEEE ICASSP (2005)
Vidrascu, L., Devillers, L.: Real-life emotions in naturalistic data recorded in a medical call center. In: Workshop on Emotion, LREC (2006)
Boersma, P.: Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. In: Proceedings of the Institute of Phonetic Sciences, pp. 97–110 (1993)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Devillers, L., Vidrascu, L. (2007). Real-Life Emotion Recognition in Speech. In: Müller, C. (eds) Speaker Classification II. Lecture Notes in Computer Science(), vol 4441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74122-0_4
Download citation
DOI: https://doi.org/10.1007/978-3-540-74122-0_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74121-3
Online ISBN: 978-3-540-74122-0
eBook Packages: Computer ScienceComputer Science (R0)