Multi classifier systems and forward backward feature selection algorithms to classify emotional coloured speech

ICMI 2013 - Proceedings of the 2013 ACM International Conference on Multimodal Interaction

Volumn , Issue , 2013, Pages 551-556

  Meudt, Sascha ^a   Zharkov, Dimitri ^a   Kächele, Markus ^a   Schwenker, Friedhelm ^a  

^a UNIVERSITY OF ULM   (Germany)

Author keywords

affective computing; emotion recognition; feature selection; human computer interaction; multi classifier systems

Indexed keywords

AFFECTIVE COMPUTING; BACKWARD FEATURE SELECTIONS; EMOTION RECOGNITION; MULTICLASSIFIER SYSTEM; PSYCHOLOGICAL CHARACTERISTICS; REAL-WORLD DATASETS; REAL-WORLD SCENARIO; UNCONSTRAINED ENVIRONMENTS;

ALGORITHMS; FEATURE EXTRACTION; INTERACTIVE COMPUTER SYSTEMS;

HUMAN COMPUTER INTERACTION;

EID: 84892570615     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2522848.2531743     Document Type: Conference Paper

References (33)

1. M. Airas and P. Alku. Comparison of multiple voice source parameters in different phonation types. In INTERSPEECH, pages 1410-1413, 2007.
2. P. Alku, T. Bäckström, and E. Vilkman. Normalized amplitude quotient for parametrization of the glottal flow. the Journal of the Acoustical Society of America, 112:701, 2002.
3. F. Burkhardt, A. Paeschke, M. Rolfes, W. Sendlmeier, and B. Weiss. A database of german emotional speech. In Proceedings of Interspeech 2005, 2005.
4. A. Dhall, R. Goecke, J. Joshi, M. Wagner, and T. Gedeon. Emotion recognition in the wild challenge. ACM ICMI, 2013.
5. A. Dhall, R. Goecke, S. Lucey, and T. Gedeon. Collecting large, richly annotated facial-expression databases from movies. IEEE Multimedia, 19:34-41, 2012.
6. T. Drugman, B. Bozkurt, and T. Dutoit. Causal-anticausal decomposition of speech using complex cepstrum for glottal source estimation. Speech Communication, 53(6):855-866, 2011.
7. G. Fant, J. Liljencrants, and Q.-g. Lin. A four-parameter model of glottal flow. STL-QPSR, 4(1985):1-13, 1985.
8. N. Fragopanagos and J. Taylor. Emotion recognition in human-computer interaction. Neural Networks, 18:389-405, 2005.
9. N. H. Frijda. Recognition of emotion. Advances in experimental social psychology, 4:167-223, 1969.
10. H. Hermansky, N. Morgan, A. Bayya, and P. Kohn. Rasta-plp speech analysis technique. In Acoustics, Speech, and Signal Processing. ICASSP-92., IEEE International Conference on, volume 1, pages 121-124. IEEE, 1992.
11. T. Kanade, J. Cohn, and Y. Tian. Comprehensive database for facial expression analysis. In Automatic Face and Gesture Recognition, 2000. Proceedings. Fourth IEEE International Conference on, pages 46-53, 2000.
12. J. Kane and C. Gobl. Identifying regions of non-modal phonation using features of the wavelet transform. In INTERSPEECH, pages 177-180, 2011.
13. J. Kane and C. Gobl. Wavelet maxima dispersion for breathy to tense voice discrimination. IEEE Transactions on Audio, Speech, and Language Processing, 21, 2013.
14. N. Kanedera, T. Arai, H. Hermansky, and M. Pavel. On the relative importance of various components of the modulation spectrum for automatic speech recognition. Speech Communication, 28(1):43-55, 1999.
15. C. M. Lee, S. Yildirim, M. Bulut, A. Kazemzadeh, C. Busso, Z. Deng, S. Lee, and S. S. Narayanan. Emotion recognition based on phoneme classes. In Proceedings of ICSLP 04, 2004.
16. I. Luengo, E. Navas, and I. Hernáez. Feature analysis and evaluation for automatic emotion identification in speech. Multimedia, IEEE Transactions on, 12(6):490-501, 2010.
17. M. Lugger and B. Yang. Classification of different speaking groups by means of voice quality parameters. ITG-Fachbericht-Sprachkommunikation, 2006.
18. M. Lugger and B. Yang. The relevance of voice quality features in speaker independent emotion recognition. In IEEE International Conference on Acoustics, Speech and Signal Processing. ICASSP., volume 4, pages IV-17. IEEE, 2007.
19. S. Meudt and F. Schwenker. On instance selection in audio based emotion recognition. In Proceedings of the 5th IAPR TC3 Workshop on Artificial Neural Networks for Pattern Recognition (ANNPR'12), pages 186-192. Springer, 2012.
20. J. Nicholson, K. Takahashi, and R. Nakatsu. Emotion recognition in speech using neural networks. Neural Computing and Applications, 9:290-296, 2000.
21. T. L. Nwe, S. W. Foo, and L. C. De Silva. Speech emotion recognition using hidden markov models. Speech communication, 41(4):603-623, 2003.
22. G. Palm and F. Schwenker. Sensor-fusion in neural networks. In E. Shahbazian, G. Rogova, and M. J. DeWeert, editors, Harbour Protection Through Data Fusion Technologies, pages 299-306. Springer, 2009.
23. L. Rabiner and B.-H. Juang. Fundamentals of speech recognition. 1993.
24. S. Schachter. The interaction of cognitive and physiological determinants of emotional state. Advances in Experimental Social Psychology, 1(Bd. 1):49-80, 1964.
25. K. R. Scherer, T. Johnstone, and G. Klasmeyer. Handbook of Affective Sciences - Vocal expression of emotion, chapter 23, pages 433-456. Affective Science. Oxford University Press, 2003.
26. S. Scherer, J. Kane, C. Gobl, and F. Schwenker. Investigating fuzzy-input fuzzy-output support vector machines for robust voice quality classification. Computer Speech and Language, 27(1):263-287, Jan. 2012.
27. S. Scherer, M. Oubbati, F. Schwenker, and G. Palm. Real-time emotion recognition from speech using echo state networks. In Artificial Neural Networks in Pattern Recognition, pages 205-216. Springer Berlin Heidelberg, 2008.
28. S. Scherer, F. Schwenker, and G. Palm. Classifier fusion for emotion recognition from speech. In Proceedings of Intelligent Environments 07, 2007.
29. S. Scherer, F. Schwenker, and G. Palm. Emotion recognition from speech using multi-classifier systems and rbf-ensembles. In Speech, Audio, Image and Biomedical Signal Processing using Neural Networks, pages 49-70. Springer Berlin Heidelberg, 2008.
30. S. Scherer, F. Schwenker, and G. Palm. Classifier fusion for emotion recognition from speech. In W. Minker, M. Weber, H. Hagras, V. Callagan, and A. D. Kameas, editors, Advanced Intelligent Environments, pages 95-117. Springer, 2009.
31. F. Schwenker, S. Scherer, M. Schmidt, M. Schels, and M. Glodek. Multiple classifier systems for the recognition of human emotions. In N. E. Gayar, J. Kittler, and F. Roli, editors, Proceedings of the 9th International Workshop on Multiple Classifier Systems (MCS'10), LNCS 5997, pages 315-324. Springer, 2010.
32. M. Valstar, B. Schuller, K. Smith, F. Eyben, B. Jiang, S. Bilakhia, S. Schnieder, R. Cowie, and M. Pantic. Avec 2013-the continuous audio/visual emotion and depression recognition challenge.
33. S. Walter, S. Scherer, M. Schels, M. Glodek, D. Hrabal, M. Schmidt, R. Böck, K. Limbrecht, H. Traue, and F. Schwenker. Multimodal emotion classification in naturalistic user behavior. In J. Jacko, editor, Human-Computer Interaction. Towards Mobile and Intelligent Interaction Environments, volume 6763 of Lecture Notes in Computer Science, pages 603-611. Springer Berlin Heidelberg, 2011.