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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...
An important pre-processing stage in speech recognition systems is that of extracting phonetically pertinent acoustic features from the speech signal. These features form the basis for discriminative classification and serve as cues for the identification of phonetic events in speech. The paper addresses this by presenting a novel method for the classification of harmonic (short-term periodic) and...
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