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Study on Speech Emotion Recognition System in E-Learning

  • Aiqin Zhu
  • Qi Luo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4552)

Abstract

Aiming at emotion deficiency in present E-Learning system, speech emotion recognition system is proposed in the paper. A corpus of emotional speech from various subjects, speaking different languages is collected for developing and testing the feasibility of the system. The potential prosodic features are first identified and extracted from the speech data. Then we introduce a systematic feature selection approach which involves the application of Sequential Forward Selection (SFS) with a General Regression Neural Network (GRNN) in conjunction with a consistency-based selection method. The selected features are employed as the input to a Modular Neural Network (MNN) to realize the classification of emotions. Our simulation experiment results show that the proposed system gives high recognition performance.

Keywords

E-learning SFS GRNN MNN Affective computing 

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References

  1. 1.
    Kekang, H.: E-Learning Essence- information technology into curriculum. E-education Research 105(1), 3–4 (2002)Google Scholar
  2. 2.
    Jijun, W.: Emotion deficiency and compensation in distance learning. Chinese network education (2005)Google Scholar
  3. 3.
    Picard, R.W.: Affective Computing. MIT Press, Cambridge (1997)Google Scholar
  4. 4.
    Picard, R.W.: Affective Computing, Challenges. Cambridge. International Journal of Human Computer studies 59(1), 55–64 (2003)CrossRefGoogle Scholar
  5. 5.
    Dellaert, F., Polzin, T., Waibel, A.: Recognizing Emotion in Speech. In: Proceedings of the ICSLP 1996 (1996)Google Scholar
  6. 6.
    Nicholson, J., Takabashi, K., Nakatsu, R.: Emotion Recognition in Speech Using Neural Network. Neural Information Processing (1999)Google Scholar
  7. 7.
    Kittler, J.: Feature Set Search Algorithms. Pattern Recognition and Signal Processing, 41–60 (1978)Google Scholar
  8. 8.
    Specht, D.F.: A general regression neural network. IEEE Trans. Neural Networks 2(6), 568–576 (1991)CrossRefGoogle Scholar
  9. 9.
    Wu, M.: Dynamic resource allocation via video content and short-term statistics. In: IEEE Int. Conf. on Image Processing, vol. III, pp. 58–61 (2000)Google Scholar
  10. 10.
    Cai, Q., Mitche, A., Aggarwal, J.K.: Track Human Motion in an Indoor Environment. In: Proc.of 2nd Intl.Conf.Image Processing (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Aiqin Zhu
    • 1
  • Qi Luo
    • 2
  1. 1.College of Urban and Environment Science,Central China Normal University, Wuhan, 430079, HubeiChina
  2. 2.Wuhan University of Science and Technology Zhongnan Branch, Wuhan, 430223, HubeiChina

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