ISCA Archive Interspeech 2017
ISCA Archive Interspeech 2017

An Investigation of Emotion Prediction Uncertainty Using Gaussian Mixture Regression

Ting Dang, Vidhyasaharan Sethu, Julien Epps, Eliathamby Ambikairajah

Existing continuous emotion prediction systems implicitly assume that prediction certainty does not vary with time. However, perception differences among raters and other possible sources of variability suggest that prediction certainty varies with time, which warrants deeper consideration. In this paper, the correlation between the inter-rater variability and the uncertainty of predicted emotion is firstly studied. A new paradigm that estimates the uncertainty in prediction is proposed based on the strong correlation uncovered in the RECOLA database. This is implemented by including the inter-rater variability as a representation of the uncertainty information in a probabilistic Gaussian Mixture Regression (GMR) model. In addition, we investigate the correlation between the uncertainty and the performance of a typical emotion prediction system utilizing average rating as the ground truth, by comparing the prediction performance in the lower and higher uncertainty regions. As expected, it is observed that the performance in lower uncertainty regions is better than that in higher uncertainty regions, providing a path for improving emotion prediction systems.

doi: 10.21437/Interspeech.2017-512

Cite as: Dang, T., Sethu, V., Epps, J., Ambikairajah, E. (2017) An Investigation of Emotion Prediction Uncertainty Using Gaussian Mixture Regression. Proc. Interspeech 2017, 1248-1252, doi: 10.21437/Interspeech.2017-512

  author={Ting Dang and Vidhyasaharan Sethu and Julien Epps and Eliathamby Ambikairajah},
  title={{An Investigation of Emotion Prediction Uncertainty Using Gaussian Mixture Regression}},
  booktitle={Proc. Interspeech 2017},