Table of Contents
ISRN Signal Processing
Volume 2011 (2011), Article ID 753819, 15 pages
http://dx.doi.org/10.5402/2011/753819
Research Article

Classification of Emotional Speech Based on an Automatically Elaborated Hierarchical Classifier

1Université de Lyon, CNRS, Ecole Centrale de Lyon, LIRIS, UMR5205, 69134, France
2School of Physical Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China
3Tsinghua National Laboratory for Information Science and Technology, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Received 4 November 2010; Accepted 15 December 2010

Academic Editors: Y. H. Ha, R. Palaniappan, and F. Palmieri

Copyright © 2011 Zhongzhe Xiao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. http://emotion-research.net.
  2. R. Picard, Affective Computing, MIT Press, Cambridge, Mass, USA, 1997.
  3. Z. Zeng, M. Pantic, G. I. Roisman, and T. S. Huang, “A survey of affect recognition methods: audio, visual, and spontaneous expressions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 1, pp. 39–58, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. J. Hirschberg, S. Benus, J. M. Brenier et al., “Distinguishing deceptive from non-deceptive speech,” in Proceedings of the 9th European Conference on Speech Communication and Technology (INTERSPEECH '05), pp. 1833–1836, September 2005. View at Scopus
  5. J. Liscombe, J. Hirschberg, and J. J. Venditti, “Detecting certainness in spoken tutorial dialogues,” in Proceedings of the 9th European Conference on Speech Communication and Technology (INTERSPEECH '05), pp. 1837–1840, September 2005. View at Scopus
  6. O. W. Kwon, K. Chan, J. Hao, and T. W. Lee, “Emotion recognition by speech signals,” in Proceedings of the 8th European Conference on Speech Communication and Technology (EUROSPEECH '03), Geneva, Switzerland, September 2003.
  7. T. Zhang, M. Hasegawa-Johnson, and S. E. Levinson, “Children’s Emotion Recognition in an Intelligent Tutoring Scenario,” in Proceedings of the 8th European Conference on Speech Communication and Technology (INTERSPEECH '04), 2004.
  8. T. Vogt and E. André, “Comparing feature sets for acted and spontaneous speech in view of automatic emotion recognition,” in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME '05), pp. 474–477, July 2005. View at Publisher · View at Google Scholar · View at Scopus
  9. B. Schuller, M. Wimmer, L. Mösenlechner, C. Kern, D. Arsic, and G. Rigoll, “Brute-forcing hierarchical functionals for paralinguistics: a waste of feature space?” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '08), pp. 4501–4504, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Cowie, E. Douglas-Cowie, N. Tsapatsoulis et al., “Emotion recognition in human-computer interaction,” IEEE Signal Processing Magazine, vol. 18, no. 1, pp. 32–80, 2001. View at Publisher · View at Google Scholar · View at Scopus
  11. Z. Xiao, E. Dellandrea, L. Chen, and W. Dou, “Recognition of emotions in speech by a hierarchical approach,” in Proceedings of the 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops (ACII '09), Amsterdam, The Netherlands, September 2009. View at Publisher · View at Google Scholar
  12. R. Banse and K. R. Scherer, “Acoustic profiles in vocal emotion expression,” Journal of Personality and Social Psychology, vol. 70, no. 3, pp. 614–636, 1996. View at Google Scholar · View at Scopus
  13. B. Schuller, G. Rigol, and M. Lang, “Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine—belief network architecture,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '04), pp. I577–I580, May 2004. View at Scopus
  14. B. Schuller, S. Reiter, R. Muller, M. Al-Hames, M. Lang, and G. Rigoll, “Speaker independent speech emotion recognition by ensemble classification,” in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME '05), pp. 864–867, July 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. D. Morrison and L. C. De Silva, “Voting ensembles for spoken affect classification,” Journal of Network and Computer Applications, vol. 30, no. 4, pp. 1356–1365, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. Z. Xiao, E. Dellandrea, W. Dou, and L. Chen, “Multi-stage classification of emotional speech motivated by a dimensional emotion model,” Multimedia Tools and Applications, vol. 46, no. 1, pp. 119–145, 2010. View at Publisher · View at Google Scholar
  17. D. Ververidis and C. Kotropoulos, “Emotional speech classification using Gaussian mixture models and the sequential floating forward selection algorithm,” in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME '05), pp. 1500–1503, July 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. B. Schuller, S. Reiter, and G. Rigoll, “Evolutionary feature generation in speech emotion recognition,” in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME '06), pp. 5–8, July 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. R. Kohavi and G. H. John, “Wrappers for feature subset selection,” Artificial Intelligence, vol. 97, no. 1-2, pp. 273–324, 1997. View at Google Scholar
  20. I. Guyon and A. Elisseff, “An introduction to variable and feature selection,” Journal of Machine Learning Research, vol. 3, pp. 1157–1182, 2003. View at Google Scholar
  21. M. Sebban and R. Nock, “A hybrid filter/wrapper approach of feature selection using information theory,” in Proceedings of International Conference on Machine Learning and Cybernetics, vol. 4, pp. 2537–2542, 2004.
  22. J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, Calif, USA, 1993.
  23. J. R. Quinlan, “Improved use of continuous attributes in C4.5,” Journal of Artificial Intelligence Research, vol. 4, pp. 77–90, 1996. View at Google Scholar · View at Scopus
  24. A. P. Dempster, “Upper and lower probabilities induced by a multivalued mapping,” Annals of Mathematical Statistics, vol. 38, no. 2, pp. 325–339, 1967. View at Publisher · View at Google Scholar
  25. A. P. Dempster, “A generalization of Bayesian inference,” Journal of the Royal Statistical Society B, vol. 30, 1968. View at Google Scholar
  26. G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, Princeton, NJ, USA, 1976.
  27. G. Fioretti, “Evidence theory: a mathematical framework for unpredictable hypotheses,” Metroeconomica, vol. 55, no. 4, pp. 345–366, 2004. View at Google Scholar
  28. A. L. Blum and P. Langley, “Selection of relevant features and examples in machine learning,” Artificial Intelligence, vol. 97, no. 1-2, pp. 245–271, 1997. View at Google Scholar · View at Scopus
  29. M. Detyniecki, Mathematical aggregation operators and their application to video querying, Doctoral thesis, University of Paris 6, France, LIP6 research report 2001/002, November 2000.
  30. K. Menger, “Statistical metrics,” Proceedings of the National Academy of Sciences of the United States of America, vol. 8, pp. 535–537, 1942. View at Google Scholar
  31. B. Schweizer and A. Sklar, “Statistical metric spaces,” Pacific Journal of Mathematics, vol. 10, pp. 313–334, 1960. View at Google Scholar
  32. B. Schweizer and A. Sklar, Probabilistic Metric Spaces, North Holland, New York, NY, USA, 1983.
  33. R. Fuller, “OWA operators in decision making,” in Exploring the Limits of Support Systems, C. Carlsson, Ed., vol. 3 of TUCS General Publications, pp. 85–104, 1996. View at Google Scholar
  34. W. Dou, Segmentation of multispectral images based on information fusion: application for MRI images, Ph.D. thesis, Université de Caen, 2006.
  35. H. Fu, Z. Xiao, E. Dellandréa, W. Dou, and L. Chen, “Image categorization using ESFS: a new embedded feature selection method based on evidence theory,” in Proceedings of the International Conference on Advanced Concepts Intelligent Vision Systems (ACIVS '09), Bordeaux, France, September 2009. View at Publisher · View at Google Scholar
  36. D. Ververidis, C. Kotropoulos, and I. Pitas, “Automatic emotional speech classification,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '04), vol. 1, pp. I593–I596, Montreal, Canada, May 2004.
  37. D. Ververidis and C. Kotropoulos, “Automatic speech classification to five emotional states based on gender information,” in Proceedings of 12th European Signal Processing Conference, pp. 341–344, Austria, September 2004.
  38. D. Ververidis and C. Kotropoulos, “Emotional speech classification using Gaussian mixture models,” in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS '05), pp. 2871–2874, May 2005. View at Publisher · View at Google Scholar · View at Scopus
  39. H. Harb and L. Chen, “Voice-based gender identification in multimedia applications,” Journal of Intelligent Information Systems, vol. 24, no. 2-3, pp. 179–198, 2005. View at Publisher · View at Google Scholar · View at Scopus
  40. Z. Xiao, Recognition of emotion in audio signals, Ph.D. thesis, Ecole Centrale de Lyon, 2008.
  41. Z. Xiao, E. Dellandrea, W. Dou, and L. Chen, “Ambiguous classification of emotional speech,” in Proceedings of the International Workshop on EMOTION—Satellite of International Conference on Language Resources and Evaluation (LREC '08), 2008.