A speaker independent approach to the classification of emotional vocal expressions
H Atassi, A Esposito - 2008 20th IEEE international conference …, 2008 - ieeexplore.ieee.org
H Atassi, A Esposito
2008 20th IEEE international conference on tools with artificial …, 2008•ieeexplore.ieee.orgThe 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 are selected for further processing in order to
discriminate between them. For this purpose, a unique set of high-level acoustic features …
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 are selected for further processing in order to
discriminate between them. For this purpose, a unique set of high-level acoustic features …
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 are selected for further processing in order to discriminate between them. For this purpose, a unique set of high-level acoustic features was identified using the Sequential Floating Forward Selection (SFFS) algorithm, and a GMM was used to separate between each couple of emotion. The mean classification rate is 81% with an improvement of 5% with respect to the most recent results obtained on the same database (75%).
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