Gaussian mixture models with class-dependent features for speech emotion recognition

In this paper, we propose models for emotion recognition from speech based on class-dependent features and Gaussian mixture models (GMM). Seven emotions are identified (Happiness, Fear, Neutral, Disgust, Anger, Boredom and Sadness) with a small set of features for each class. Results show that our system outperforms the single-stage classifier, with a 82.41% (74.86% in single-stage) overall recognition rate for the male case and 81.28% (71.82% in single-stage) for the female case.