Abstract
Combining information from multiple sources is a vivid field of research. The problem of emotion recognition is inherently multi-modal. As automatic recognition of the emotional states is performed imperfectly by the single mode classifiers, its combination is crucial. In this work, the AVEC 2011 corpus is used to evaluate several machine learning techniques in the context of information fusion. In particular temporal integration of intermediate results combined with a reject option based on classifier confidences. The results for the modes are combined using a Markov random field that is designed to be able to tackle failures of individual channels.
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References
Besag, J.: On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society. Series B (Methodological) 24(3), 259–302 (1986)
Bicego, M., Murino, V., Figueiredo, M.A.T.: Similarity-Based Clustering of Sequences Using Hidden Markov Models. In: Perner, P., Rosenfeld, A. (eds.) MLDM 2003. LNCS (LNAI), vol. 2734, pp. 86–95. Springer, Heidelberg (2003)
Bishop, C.M.: Pattern Recognition and Machine Learning, vol. 4, ch. 8.3, pp. 383–392. Springer (2006)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Chow, C.: On optimum recognition error and reject tradeoff. IEEE Transactions on Information Theory 16(1), 41–46 (1970)
De Stefano, C., Sansone, C., Vento, M.: To reject or not to reject: that is the question-an answer in case of neural classifiers. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 30(1), 84–94 (2000)
Hermansky, H., Morgan, N., Bayya, A., Kohn, P.: RASTA-PLP speech analysis technique. In: Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing, vol. 1, pp. 121–124. IEEE (1992)
Littlewort, G., Whitehill, J., Wu, T., Fasel, I., Frank, M., Movellan, J., Bartlett, M.: The computer expression recognition toolbox (CERT). In: Proc. of the Int. Conf. on Automatic Face & Gesture Recognition and Workshops, pp. 298–305. IEEE (2011)
McKeown, G., Valstar, M.F., Cowie, R., Pantic, M.: The SEMAINE corpus of emotionally coloured character interactions. In: Proc. of the Int. Conf. on Multimedia and Expo (ICME), pp. 1079–1084. IEEE (2010)
Rabiner, L., Juang, B.H.: Fundamentals of speech recognition. Prentice Hall (1993)
Schuller, B., Valstar, M., Eyben, F., McKeown, G., Cowie, R., Pantic, M.: AVEC 2011–The First International Audio/Visual Emotion Challenge. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011, Part II. LNCS, vol. 6975, pp. 415–424. Springer, Heidelberg (2011)
Thiel, C., Schwenker, F., Palm, G.: Using Dempster-Shafer Theory in MCF Systems to Reject Samples. In: Oza, N.C., Polikar, R., Kittler, J., Roli, F. (eds.) MCS 2005. LNCS, vol. 3541, pp. 118–127. Springer, Heidelberg (2005)
Zheng, F., Zhang, G., Song, Z.: Comparison of different implementations of MFCC. Journal of Computer Science and Technology 16(6), 582–589 (2001)
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Schels, M., Glodek, M., Schwenker, F., Palm, G. (2013). Revisiting AVEC 2011 – An Information Fusion Architecture. In: Apolloni, B., Bassis, S., Esposito, A., Morabito, F. (eds) Neural Nets and Surroundings. Smart Innovation, Systems and Technologies, vol 19. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35467-0_38
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DOI: https://doi.org/10.1007/978-3-642-35467-0_38
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