The Interactive Feature Selection Method Development for an ANN Based Emotion Recognition System
This paper presents an original feature selection method for Emotion Recognition which includes many original elements. Feature selection has some merit regarding pattern recognition performance. Thus, we implemented a simulator called an ’IFS system’ and the results of the IFS were applied to an emotion recognition system(ERS). Our innovative feature selection method was based on a Reinforcement Learning Algorithm and since it required responses from human users, it was denoted an ’Interactive Feature Selection’. By performing an IFS, we were able to obtain three top features and apply them to the ERS.
KeywordsFeature Selection Speech Signal Emotion Recognition Feature Selection Method Speech Rate
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- 1.Ververidis, D., Kotropoulos, C.: Emotional speech classification using Gaussian mixture models. In: Proceedings of ISCAS, vol. 3, pp. 2871–2874 (May 2005)Google Scholar
- 3.Wagner, J., Kim, J.H., Andre, E.: From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification. In: Proceedings of ICME, pp. 940–943 (July 2005)Google Scholar
- 4.Pudil, P., Novovicova, J.: Novel Methods for Subset Selection with Respect to Problem knowledge. IEEE Intelligent Systems, 66–74 (March 1998)Google Scholar
- 8.Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. A bradford book, London (1998)Google Scholar
- 9.Park, C.H., Sim, K.B.: The Implementation of the Emotion Recognition from Speech and Facial Expression System. In: Proc. of ICNC 2005-FSKD 2005, pp. 85–88 (August 2005)Google Scholar