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2011
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(2011) : A novel real-time emotion detection system from audio streams based on Bayesian Quadratic Discriminate Classifier for ADAS: Proceedings of the Joint INDS’11 ISTET’11: Klagenfurt, Austria: IEEE, S. 1-5
DOI: https://doi.org/10.1109/INDS.2011.6024783 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.
Keywords: ADAS, Advanced driver assistance systems, Angemessen(heit) (von Technik), audio signal processing, audio streaming, audio streams, Bayes methods, Bayesian methods, Bayesian quadratic discriminate classifier, driver information systems, emotion feature extraction, emotion recognition, Feature extraction, ieee xplore, real-time emotion detection system, real-time emotion recognition concept, road accidents, speech, speech recognition, Support vector machines, traffic accidents, Vehicles, voice streams
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