AUTOMATIC CLASSIFICATION OF EMOTIONS AND INTER-LABELER CONSISTENCY

@inproceedings{SteidlAUTOMATICCO,
  title={AUTOMATIC CLASSIFICATION OF EMOTIONS AND INTER-LABELER CONSISTENCY},
  author={Stefan Steidl and Michael Levit and Anton Batliner and Elmar N̈oth and Heinrich Niemann}
}
In traditional classification problems, the reference needed for training a classifier is given and considered to be absolutely correct. However, this does not apply to all tasks. In emotion recognition in non-acted speech, for instance, one often does not know which emotion was really intended by the speaker. Hence, the data is annotated by a group of human labelers who do not agree on one common class in most cases. Often, similar classes are confused systematically. We propose a new entropy… CONTINUE READING

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