Multimodal Affect Recognition in the Context of Human-Computer Interaction for Companion-Systems

  • Friedhelm Schwenker
  • Ronald Böck
  • Martin Schels
  • Sascha Meudt
  • Ingo Siegert
  • Michael Glodek
  • Markus Kächele
  • Miriam Schmidt-Wack
  • Patrick Thiam
  • Andreas Wendemuth
  • Gerald Krell
Chapter
Part of the Cognitive Technologies book series (COGTECH)

Abstract

In general, humans interact with each other using multiple modalities. The main channels are speech, facial expressions, and gesture. But also bio-physiological data such as biopotentials can convey valuable information which can be used to interpret the communication in a dedicated way. A Companion-System can use these modalities to perform an efficient human-computer interaction (HCI). To do so, the multiple sources need to be analyzed and combined in technical systems. However, so far only few studies have been published dealing with the fusion of three or even more such modalities. This chapter addresses the necessary processing steps in the development of a multimodal system applying fusion approaches.

ATLAS and ikannotate are presented which are designed for the pre-analyzing of multimodal data streams and the labeling of relevant parts. ATLAS allows us to display raw data, extracted features and even outputs of pre-trained classifier modules. Further, the tool integrates annotation, transcription and an active learning module. Ikannotate can be directly used for transcription and guided step-wise emotional annotation of multimodal data. The tool includes the three mainly used annotation paradigms, namely the basic emotions, the Geneva emotion wheel and the self-assessment manikins (SAMs). Furthermore, annotators using ikannotate can assign an uncertainty to samples.

Classifier architectures need to realize a fusion system in which the multiple modalities are combined. A large number of machine learning approaches were evaluated, such as data, feature, score and decision-level fusion schemes, but also temporal fusion architectures and partially supervised learning.

The proposed methods are evaluated on either multimodal benchmark corpora or on the datasets of the Transregional Collaborative Research Centre SFB/TRR 62, i.e. Last Minute Corpus and the EmoRec Dataset. Furthermore, we present results which were achieved in international challenges.

Notes

Acknowledgements

We thank our highly regarded deceased colleague and friend Prof. Dr. Bernd Michaelis who contributed to the SFB on various topics and provided well-informed suggestions. This work was done within the Transregional Collaborative Research Centre SFB/TRR 62 “Companion-Technology for Cognitive Technical Systems” funded by the German Research Foundation (DFG).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Friedhelm Schwenker
    • 1
  • Ronald Böck
    • 2
  • Martin Schels
    • 1
  • Sascha Meudt
    • 1
  • Ingo Siegert
    • 2
  • Michael Glodek
    • 1
  • Markus Kächele
    • 1
  • Miriam Schmidt-Wack
    • 1
  • Patrick Thiam
    • 1
  • Andreas Wendemuth
    • 2
    • 3
  • Gerald Krell
    • 4
  1. 1.Institute for Neural Information ProcessingUniversity of UlmUlmGermany
  2. 2.Cognitive Systems Group, Institute for Information Technology and CommunicationsOtto von Guericke UniversityMagdeburgGermany
  3. 3.Center for Behavioral Brain SciencesMagdeburgGermany
  4. 4.Technical Computer Science Group, Institute for Information Technology and CommunicationsOtto von Guericke UniversityMagdeburgGermany

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