Modelling Sample Informativeness for Deep Affective Computing

Georgios Rizos, Bjorn Schuller

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Using data with high quality annotation is crucial in emotion recognition applications, especially because the task is subjective and the raters may exhibit disagreement with respect to each sample. In this paper, we propose a meta-learning methodology that can reason about the training data and detect potentially less informative instances in order to reduce their impact in the training process. The way we inform the meta-learner on the importance of each sample is by utilising recent advances in uncertainty modelling with Bayesian neural networks that can decompose predictive uncertainty into: a) model uncertainty that is due to a lack of observations and b) label uncertainty that is due to inherent randomness in the data labelling, which we adapt for affective computing. Our proposed method for soft data selection exhibits a 6% absolute improvement in Concordance Correlation Coefficient with respect to the baseline in a two-dimensional continuous affect recognition task.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3482-3486
Number of pages5
ISBN (Electronic)9781479981311
DOIs
StatePublished - May 2019
Externally publishedYes
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: 12 May 201917 May 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/1917/05/19

Keywords

  • affect recognition
  • annotation quality
  • Bayesian neural networks
  • meta-learning
  • soft data selection

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