Emotion recognition from speech VIA boosted Gaussian mixture models

@article{Tang2009EmotionRF,
  title={Emotion recognition from speech VIA boosted Gaussian mixture models},
  author={Hao Tang and Stephen M. Chu and Mark Hasegawa-Johnson and Thomas S. Huang},
  journal={2009 IEEE International Conference on Multimedia and Expo},
  year={2009},
  pages={294-297}
}
Gaussian mixture models (GMMs) and the minimum error rate classifier (i.e. Bayesian optimal classifier) are popular and effective tools for speech emotion recognition. Typically, GMMs are used to model the class-conditional distributions of acoustic features and their parameters are estimated by the expectation maximization (EM) algorithm based on a training data set. Then, classification is performed to minimize the classification error w.r.t. the estimated class-conditional distributions. We… CONTINUE READING
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