Spoken emotion recognition through optimum-path forest classification using glottal features

@article{Iliev2010SpokenER,
  title={Spoken emotion recognition through optimum-path forest classification using glottal features},
  author={Alexander I. Iliev and Michael S. Scordilis and Jo{\~a}o Paulo Papa and Alexandre X. Falc{\~a}o},
  journal={Computer Speech & Language},
  year={2010},
  volume={24},
  pages={445-460}
}
A new method for the recognition of spoken emotions is presented based on features of the glottal airflow signal. Its effectiveness is tested on the new optimum path classifier (OPF) as well as on six other previously established classification methods that included the Gaussian mixture model (GMM), support vector machine (SVM), artificial neural networks - multi layer perceptron (ANN-MLP), k-nearest neighbor rule (k-NN), Bayesian classifier (BC) and the C4.5 decision tree. The speech database… CONTINUE READING

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