Implicit Fusion by Joint Audiovisual Training for Emotion Recognition in Mono Modality

Jing Han, Zixing Zhang, Zhao Ren, Bjorn Schuller

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

36 Scopus citations

Abstract

Despite significant advances in emotion recognition from one individual modality, previous studies fail to take advantage of other modalities to train models in mono-modal scenarios. In this work, we propose a novel joint training model which implicitly fuses audio and visual information in the training procedure for either speech or facial emotion recognition. Specifically, the model consists of one modality-specific network per individual modality and one shared network to map both audio and visual cues into final predictions. In the training process, we additionally take the loss from one auxiliary modality into account besides the main modality. To evaluate the effectiveness of the implicit fusion model, we conduct extensive experiments for mono-modal emotion classification and regression, and find that the implicit fusion models outperform the standard mono-modal training process.

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.
Pages5861-5865
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

  • Joint training
  • audiovisual learning
  • emotion classification
  • emotion regression

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