DocumentCode :
683728
Title :
Emotion Recognition Modulating the Behavior of Intelligent Systems
Author :
Smailagic, Asim ; Siewiorek, Daniel ; Rudnicky, Alex ; Chakravarthula, Sandeep Nallan ; Kar, Asutosh ; Jagdale, Nivedita ; Gautam, Saumya ; Vijayaraghavan, R. ; Jagtap, Sachin
Author_Institution :
Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
fYear :
2013
fDate :
9-11 Dec. 2013
Firstpage :
378
Lastpage :
383
Abstract :
The paper presents an audio-based emotion recognition system that is able to classify emotions as anger, fear, happy, neutral, sadness or disgust in real time. We use the virtual coach as an application example of how emotion recognition can be used to modulate intelligent systems´ behavior. A novel minimum-error feature removal mechanism to reduce bandwidth and increase accuracy of our emotion recognition system has been introduced. A two-stage hierarchical classification approach along with a One-Against-All (OAA) framework are used. We obtained an average accuracy of 82.07% using the OAA approach, and 87.70% with a two-stage hierarchical approach, by pruning the feature set and using Support Vector Machines (SVMs) for classification.
Keywords :
audio signal processing; emotion recognition; knowledge based systems; pattern classification; support vector machines; OAA framework; SVM; audio-based emotion recognition system; classification; emotion recognition modulatiion; intelligent systems behavior; minimum-error feature removal mechanism; one-against-all framework; support vector machine; Accuracy; Emotion recognition; Feature extraction; Hidden Markov models; Mel frequency cepstral coefficient; Speech; emotion recognition; interaction design; voice and speech analysis; well-being;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia (ISM), 2013 IEEE International Symposium on
Conference_Location :
Anaheim, CA
Print_ISBN :
978-0-7695-5140-1
Type :
conf
DOI :
10.1109/ISM.2013.72
Filename :
6746824
Link To Document :
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