Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Interactive Feature selection Algorithm for Emotion recognition

감정 인식을 위한 Interactive Feature Selection(IFS) 알고리즘

  • 양현창 (중앙대학교 전자전기공학부) ;
  • 김호덕 (중앙대학교 전자전기공학부) ;
  • 박창현 (중앙대학교 전자전기공학부) ;
  • 심귀보 (중앙대학교 전자전기공학부)
  • Published : 2006.12.25
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Abstract

This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and 'the curse of dimension'. Thus, We implemented a simulator called 'IFS' and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called 'Interactive Feature Selection'. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.

본 논문은 일반적으로 많은 특징들을 갖고 있는 패턴 분류 문제인 감정 인식을 위한 새로운 특징 선택 방법을 제안한다. '특징 선택'은 패턴 인식 성능의 향상에 기여하고 '차원의 저주'문제에도 좋은 해결책으로 많이 사용되는 방법이다. 그래서, 본 논문에서는 강화학습의 개념을 사용한 상호 작용에 의한 특징 선택 방법인 IFS(Interactiv Feature Selection)를 고안하였고 이 알고리즘을 사용하여 선택된 특징들을 감정 인식 시스템에 적용하여 성능이 향상됨을 확인하였다. 또한 기존의 특징 선택 방법과의 비교를 통하여 본 알고리즘의 우수성을 확인하였다.

Keywords

References

  1. D. Ververidis and C. Kotropoulos, 'Emotional speech classification using Gaussian mixture models,' Proceedings of ISCAS, vol. 3, pp. 2871-2874, May, 2005 https://doi.org/10.1109/ISCAS.2005.1465226
  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, March, 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, July, 2005 https://doi.org/10.1109/ICME.2005.1521579
  4. P. Pudil and J. Novovicova, 'Novel Methods for Subset Selection with Respect to Problem knowledge,' IEEE Intelligent Systems, pp. 66-74, March, 1998 https://doi.org/10.1109/5254.671094
  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, Aug., 2005 https://doi.org/10.1109/ICMLC.2005.1527805
  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, Jan., 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 Engineering, vol. 17, no. 9, pp. 1223-1232, Sept., 2005 https://doi.org/10.1109/TKDE.2005.149
  8. R. S. Sutton and A. G.Barto, Reinforcement Learning : An Introduction, A bradford bock, London, 1998
  9. 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 https://doi.org/10.1007/11539117_14
  10. 박창현, 김호덕, 영현창, 심귀보, '패턴 인식문제를 위한 유전자 알고리즘 기반 특징 선택 방법 개발', 한국 퍼지 및 지능시스템학회 논문지, 제16권, 제4호, pp. 466-471, 2006