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Emotion Recognition and Emotion Spotting Improvement Using Formant-Related Features

Author(s): Davood Gharavian | Mansour Sheikhan

Journal: Majlesi Journal of Electrical Engineering
ISSN 2008-1413

Volume: 4;
Issue: 4;
Start page: 1;
Date: 2010;
Original page

Keywords: Emotion recognition | formants | Gaussian mixture model

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.
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