Abstract
This paper presents a real-time emotion recognition concept of voice streams. A comprehensive solution based on Bayesian Quadratic Discriminate Classifier(QDC) is developed. The developed system supports Advanced Driver Assistance Systems (ADAS) to detect the mood of the driver based on the fact that aggressive behavior on road leads to traffic accidents. We use only 12 features to classify between 5 different classes of emotions. We illustrate that the extracted emotion features are highly overlapped and how each emotion class is effecting the recognition ratio. Finally, we show that the Bayesian Quadratic Discriminate Classifier is an appropriate solution for emotion detection systems, where a real-time detection is deeply needed with a low number of features.
The original version of this chapter was revised: Belated corrections from author have been incorporated. The erratum to this chapter is available at https://doi.org/10.1007/978-3-642-24806-1_31
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Machot, F.A., Mosa, A.H., Fasih, A., Schwarzlmüller, C., Ali, M., Kyamakya, K. (2012). A Novel Real-Time Emotion Detection System for Advanced Driver Assistance Systems. In: Unger, H., Kyamaky, K., Kacprzyk, J. (eds) Autonomous Systems: Developments and Trends. Studies in Computational Intelligence, vol 391. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24806-1_21
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DOI: https://doi.org/10.1007/978-3-642-24806-1_21
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