Abstract
This paper introduces the CASIA audio emotion recognition method for the audio sub-challenge of Audio/Visual Emotion Challenge 2011 (AVEC2011). Two popular pattern recognition techniques, SVM and AdaBoost, are adopted to solve the emotion recognition problem. The feature set is also simply investigated by comparing the performance of classifier built on the baseline feature set and the dimension reduced feature set. Experimental results show that the baseline feature set is better for the classification of arousal and power dimensions, while the reduced feature set is better for the other affective dimensions, and the average performance of AdaBoost slightly outperforms SVMs in our experiment.
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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., et al. (eds.) ACII 2011, Part II, vol. 6975, pp. 415–424. Springer, Heidelberg (2011)
Fontaine, J.R.J., Scherer, K.R., Roesch, E.B., Ellsworth, P.C.: The world of emotions is not two-dimensional. Psychological Science 18(2), 1050–1057 (2007)
McKeown, G., Valstar, M.F., Cowie, R., Pantic, M.: The SEMAINE corpus of emotionally coloured character interactions. In: 2010 IEEE International Conference on Multimedia and Expo. (ICME 2010), pp. 1079–1084 (July 2010)
Womack, B.D., Hansen, J.H.L.: Classification of speech under stress using target driven features. Speech Comm. 20, 131–150 (1996)
Womack, B.D., Hansen, J.H.L.: N-channel hidden Markov models for combined stressed speech classification and recognition. IEEE Trans. Speech Audio Processing 7(6), 668–677 (1999)
Fernandez, R., Picard, R.: Modeling drivers’ speech under stress. Speech Comm. 40, 145–159 (2003)
Dellaert, F., Polzin, T., Waibel, A.: Recognizing emotion in speech. In: Proc. International Conf. on Spoken Language Processing (ICSLP 1996), vol. 3, pp. 1970–1973 (1996)
France, D.J., Shiavi, R.G., Silverman, S., Silverman, M., Wilkes, M.: Acoustical properties of speech as indicators of depression and suicidal risk. IEEE Trans. Biomed. Eng. 7, 829–837 (2000)
Slaney, M., McRoberts, G.: Babyears: A recognition system for affective vocalizations. Speech Comm. 39, 367–384 (2003)
McGilloway, S., Cowie, R., Douglas-Cowie, E., Gielen, C.C.A.M., Westerdijk, M.J.D., Stroeve, S.H.: Approaching automatic recognition of emotion from voice: a rough benchmark. In: Proc. ISCA Workshop on Speech and Emotion, vol. 1, pp. 207–212 (2000)
Petrushin, V.A.: Emotion in speech recognition and application to call centers. In: Proc. Artificial Neural Networks in Engineering (ANNIE 1999), vol. 1, pp. 7–10 (1999)
McGilloway, S., Cowie, R., Douglas-Cowie, E., Gielen, C.C.A.M., Westerdijk, M.J.D., Stroeve, S.H.: Approaching automatic recognition of emotion from voice: a rough benchmark. In: Proc. ISCA Workshop on Speech and Emotion, vol. 1, pp. 207–212 (2000)
Freund, Y., Shapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. In: Proceedings of the Second European Conference on Computational Learning Theory, pp. 23–37 (1995)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, pp. 148–156 (1996)
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Pan, S., Tao, J., Li, Y. (2011). The CASIA Audio Emotion Recognition Method for Audio/Visual Emotion Challenge 2011. In: D’Mello, S., Graesser, A., Schuller, B., Martin, JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24571-8_50
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DOI: https://doi.org/10.1007/978-3-642-24571-8_50
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24570-1
Online ISBN: 978-3-642-24571-8
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