International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Development of Interactive Feature Selection Algorithm(IFS) for Emotion Recognition

  • Yang, Hyun-Chang (School of Electrical and Electronic Engineering, Chung-Ang University) ;
  • Kim, Ho-Duck (School of Electrical and Electronic Engineering, Chung-Ang University) ;
  • Park, Chang-Hyun (School of Electrical and Electronic Engineering, Chung-Ang University) ;
  • Sim, Kwee-Bo (School of Electrical and Electronic Engineering, Chung-Ang University)
  • Published : 2006.12.01
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Abstract

This paper presents an original feature selection method for Emotion Recognition which includes many original elements. Feature selection has some merits regarding pattern recognition performance. Thus, we developed a method called thee 'Interactive Feature Selection' and the results (selected features) of the IFS were applied to an emotion recognition system (ERS), which was also implemented in this research. The 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. Comparing those results from a random selection and Sequential Forward Selection (SFS) and Genetic Algorithm Feature Selection (GAFS), we verified that the top three features were better than the randomly selected feature set.

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References

  1. D. Ververidis and C. Kotropoulos, 'Emotional speech classification using Gaussian mixture models,' Proceedings of ISCAS, Vol. 3, pp. 2871-2874, 2005
  2. C. M. Lee and S. S. Narayanan 'Toward detecting emotions in spoken dialogs,' IEEE Transactions on Speech and Audio Processing, Vol.13, pp. 293-303, 2005 https://doi.org/10.1109/TSA.2004.838534
  3. J. Wagner, J. H. Kim, and E. Andre, 'From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification,' Proceedings of ICME, pp. 940-943, 2005
  4. P. Pudil and J. Novovicova, 'Novel Methods for Subset Selection with Respect to Problem knowledge,' IEEE Intelligent Systems, March, pp. 66-74, 1998
  5. Y. L. Lin and W. Gang, 'Speech Emotion Recognition based on HMM and SVM,' Proceedings of Machine Learning and Cybernetics, Vol.8, pp. 4898-4901, 2005
  6. F. Morchen, A. Ultsch, M. Thies, and I. Lohken, 'Modeling Timbre Distance With Temporal Statistics From Polyphonic Music,' IEEE transaction on Audio, Speech and Language Processing, Vol.14, Issue 1, pp. 81-90, 2006 https://doi.org/10.1109/TSA.2005.860352
  7. E. F. Combarro, E. Montanes, I. Diaz, J. Ranilla, and R.Mones, 'Introducing a Family of Linear Measures for Feature Selection in Text Categorization,' IEEE transactions on Knowledge and Data Engineeringl, Vol.17, No.9, pp. 1223-1232, 2005 https://doi.org/10.1109/TKDE.2005.149
  8. G. H. John, R. Kohavi, and K. Pfleger, 'Irrelevant Features and the Subset Selection Problem,' Proceedings of Machine Learning, pp. 121-129, 1998
  9. R. S. Sutton and A. G. Barto, 'Reinforcement Learning,' An Introduction, A Bradford book, London, 1998
  10. C. H. Park and K. B. Sim 'The Implementation of the Emotion Recognition from Speech and Facial Expression System,' Lecture Notes in Computer Science(LNCS), Vol. 3611, pp.85-88, 2005