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• W2117622284 abstract "Emotion has an important role in naturalness of man-machine communication. So, computerized emotion recognition from speech is investigated by many researchers in the recent decades. In this paper, the effect of formant-related features on improving the performance of emotion detection systems is experimented. To do this, various forms and combinations of the first three formants are concatenated to a popular feature vector and Gaussian mixture models are used as classifiers. Experimental results show average recognition rate of 69% in four emotional states and noticeable performance improvement by adding only one formant-related parameter to feature vector. The architecture of hybrid emotion recognition/spotting is also proposed based on the developed models." @default.
• W2117622284 created "2016-06-24" @default.
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• W2117622284 date "2010-09-22" @default.
• W2117622284 modified "2023-09-24" @default.
• W2117622284 title "EMOTION RECOGNITION AND EMOTION SPOTTING IMPROVEMENT USING FORMANT-RELATED FEATURES" @default.
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• W2117622284 doi "https://doi.org/10.1234/mjee.v4i4.266" @default.
• W2117622284 hasPublicationYear "2010" @default.
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