Abstract
Collecting and modelling real life emotions is a real challenge. However, final aim of any emotion recognition system is to identify real world emotions with reasonable accuracy. From the literature it is observed that combination of different features improves the classification performance. In this chapter score level combination of different features has been studied for recognizing real life emotions. For modelling real life emotions, there is a need of good database containing wide variety of real life emotions. In this chapter, Hindi movie database has been used to represent real world emotions. Single and multi-speaker data is collected to study the speaker influence on emotion recognition. Different features are explored for identifying the collected emotions. From the results, it is observed that spectral features carry robust emotion specific information.
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References
S.G. Koolagudi, A. Barthwal, S. Devliyal, K.S. Rao, Real life emotion classification from speech using gaussian mixture models, in Communications in Computer and Information Science: Contemporary Computing, ed. by M. Parashar, D. Kaushik, O.F. Rana, R. Samtaney, Y. Yang, A. Zomaya. vol. 306, pp. 250–261, Springer, USA, 6–8 Aug 2012
S.G. Koolagudi, S. Devliyal, A. Barthwal, K.S. Rao, Emotion recognition from semi natural speech using artificial neural networks and excitation source features, in Communications in Computer and Information Science: Contemporary Computing, ed. by M. Parashar, D. Kaushik, O.F. Rana, R. Samtaney, Y. Yang, A. Zomaya. vol. 306, pp. 273–282, Springer, USA, 6–8 Aug 2012
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Rao, K.S., Koolagudi, S.G. (2013). Emotion Recognition on Real Life Emotions. In: Robust Emotion Recognition using Spectral and Prosodic Features. SpringerBriefs in Electrical and Computer Engineering(). Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6360-3_6
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DOI: https://doi.org/10.1007/978-1-4614-6360-3_6
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