Enhanced autocorrelation in real world emotion recognition

ICMI 2014 - Proceedings of the 2014 International Conference on Multimodal Interaction

Volumn , Issue , 2014, Pages 502-507

  Meudt, Sascha ^a   Schwenker, Friedhelm ^a  

^a UNIVERSITY OF ULM   (Germany)

Author keywords

Affective computing; Audio features; Emotion recognition; Enhanced autocorrelation; Human computer interaction

Indexed keywords

AUTOCORRELATION; CONTINUOUS SPEECH RECOGNITION; INTERACTIVE COMPUTER SYSTEMS; SIGNAL PROCESSING; SPEECH PROCESSING; SPEECH RECOGNITION; VIRTUAL REALITY;

AFFECTIVE COMPUTING; AUDIO FEATURES; AUTOCORRELATION FEATURES; AUTOMATIC CLASSIFIERS; DISCRIMINATIVE FEATURES; EMOTION RECOGNITION; MULTIMODAL EMOTION RECOGNITION; REAL WORLD ENVIRONMENTS;

HUMAN COMPUTER INTERACTION;

EID: 84947261437     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2663204.2666276     Document Type: Conference Paper

References (36)

1. T. Banziger, H. Pirker, and K. Scherer. Gemep-geneva multimodal emotion portrayals: A corpus for the study of multimodal emotional expressions. In Proceedings of LREC, volume 6, pages 15{019, 2006.
2. F. Burkhardt, A. Paeschke, M. Rolfes, W. F. Sendlmeier, and B. Weiss. A database of german emotional speech. In Interspeech, volume 5, pages 1517{1520, 2005.
3. F. Dellaert, T. Polzin, and A. Waibel. Recognizing emotion in speech. In Spoken Language, 1996. ICSLP 96. Proceedings., Fourth International Conference on, volume 3, pages 1970{1973. IEEE, 1996.
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, J. Joshi, M. Wagner, and T. Gedeon. Emotion recognition in the wild challenge 2014: Baseline, data and protocol. ACM ICMI, 2014.
6. A. Dhall, R. Goecke, S. Lucey, and T. Gedeon. Collecting large, richly annotated facial-expression databases from movies. IEEE Multimedia, 19:34{41, 2012.
7. N. Fragopanagos and J. Taylor. Emotion recognition in human-computer interaction. Neural Networks, 18:389{405, 2005.
8. N. H. Frijda. Recognition of emotion. Advances in experimental social psychology, 4:167{223, 1969.
9. M. Glodek, M. Schels, G. Palm, and F. Schwenker. Multi-modal fusion based on classi-ers using rejectoptions and markov fusion networks. In Proceedings of the International Conference on Pattern Recognition (ICPR), pages 1084{1088. IEEE, 2012.
10. M. Glodek, S. Scherer, F. Schwenker, and G. Palm. Conditioned hidden markov model fusion for multimodal classification. In INTERSPEECH, pages 2269{2272, 2011.
11. M. Glodek, S. Tschechne, G. Layher, M. Schels, T. Brosch, S. Scherer, M. Kachele, M. Schmidt, H. Neumann, G. Palm, et al. Multiple classifier systems for the classification of audio-visual emotional states. In Affective Computing and Intelligent Interaction, pages 359{368. Springer, 2011.
12. H. Hermansky. The modulation spectrum in the automatic recognition of speech. In Automatic Speech Recognition and Understanding, 1997. Proceedings., 1997 IEEE Workshop on, pages 140{147. IEEE, 1997.
13. H. Hermansky, N. Morgan, A. Bayya, and P. Kohn. Rasta-plp speech analysis technique. In Acoustics, Speech, and Signal Processing, IEEE International Conference on, volume 1, pages 121{124. IEEE, 1992.
14. F. Itakura. Line spectrum representation of linear predictor coeficients of speech signals. The Journal of the Acoustical Society of America, 57(S1):S35{S35, 1975.
15. M. Kachele, M. Glodek, D. Zharkov, S. Meudt, and F. Schwenker. Fusion of audio-visual features using hierarchical classi-er systems for the recognition of affective states and the state of depression. In Proc of ICPRAM, pages 671{678, 2014.
16. T. Kanade, J. F. 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. IEEE, 2000.
17. 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.
18. B. Logan et al. Mel frequency cepstral coefficients for music modeling. In ISMIR, 2000.
19. S. Meudt and F. Schwenker. On instance selection in audio based emotion recognition. In Artificial Neural Networks in Pattern Recognition, pages 186{192. Springer, 2012.
20. S. Meudt, D. Zharkov, M. Kachele, and F. Schwenker. Multiclassifier systems and forward backward feature selection algorithms to classify emotional coloured speech. In Proceedings of the 15th ACM on International conference on multimodal interaction, pages 551{556. ACM, 2013.
21. J. Nicholson, K. Takahashi, and R. Nakatsu. Emotion recognition in speech using neural networks. Neural Computing and Applications, 9:290{296, 2000.
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. D. Rosner, J. Frommer, R. Friesen, M. Haase, J. Lange, and M. Otto. Last minute: A multimodal corpus of speech-based user-companion interactions. In LREC, pages 2559{2566, 2012.
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. M. Schels, M. Glodek, S. Meudt, S. Scherer, M. Schmidt, G. Layher, S. Tschechne, T. Brosch, D. Hrabal, S. Walter, G. Palm, H. Neumann, H. Traue, and F. Schwenker. Multi-modal classifier-fusion for the recognition of emotions. In M. Rojc and N. Campbell, editors, Coverbal synchrony in Human-Machine Interaction, chapter 4. CRC Press, 2013.
26. M. Schels, M. Kachele, M. Glodek, D. Hrabal, S. Walter, and F. Schwenker. Using unlabeled data to improve classification of emotional states in human computer interaction. Journal on Multimodal User Interfaces, 8(1):5{16, 2014.
27. 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.
28. 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.
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. B. Schuller, M. Valstar, F. Eyben, G. McKeown, R. Cowie, and M. Pantic. Avec 2011 the first international audio/visual emotion challenge. In Affective Computing and Intelligent Interaction, pages 415{424. Springer, 2011.
32. 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.
33. Y.-L. Tian, T. Kanade, and J. F. Cohn. Facial expression analysis. In Handbook of face recognition, pages 247{275. Springer, 2005.
34. T. Tolonen and M. Karjalainen. A computationally effcient multipitch analysis model. Speech and Audio Processing, IEEE Transactions on, 8(6):708{716, 2000.
35. C. Traina, A. Traina, L. Wu, and C. Faloutsos. Fast feature selection using fractal dimension. 2000.
36. S. Walter, S. Scherer, M. Schels, M. Glodek, D. Hrabal, M. Schmidt, R. Bock, K. Limbrecht, H. C. Traue, and F. Schwenker. Multimodal emotion classi-cation in naturalistic user behavior. In Human-Computer Interaction. Towards Mobile and Intelligent Interaction Environments, pages 603{611. Springer, 2011.