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Most existing research in the area of emotions recognition has focused on short segments or utterances of speech. In this paper we propose a machine learning system for classifying the overall sentiment of long conversations as being Positive or Negative. Our system has three main phases, first it divides a call into short segments, second it applies machine learning to recognize the emotion for each...
More and more efforts have been made for the research of emotional speech recently. Although we may, sometimes be able to make a definite perceptual decision on emotion state, emotion is actually a kind of cline in a large vector space. Different emotions can be thought of as zones along an emotional vector. To resolve the ambiguity of emotion perception, the authors make an array of perception experiments...
The paper proposes a speaker independent procedure for classifying vocal expressions of emotion. The procedure is based on the splitting up of the emotion recognition process into two steps. In the first step, a combination of selected acoustic features is used to classify six emotions through a Bayesian Gaussian Mixture Model classifier (GMM). The two emotions that obtain the highest likelihood scores...
To study effective speech features which can represent different emotion styles in infant voice, nonlinear features based on Teager Energy Operator are investigated. Neutral state and 4 emotional states (i.e. happiness, impatience, anger and fear) are classified from the infant voice database. MFCC extraction and HMM-based emotion classification are used as baseline system to evaluate the emotional...
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