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Incorporating Interpersonal Synchronization Features for Automatic Emotion Recognition from Visual and Audio Data

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Interpersonal features, like time-lagged synchronization, significantly improve automatic emotion recognition. This study demonstrates their benefit across visual, audio, and cross-modal analyses, enhancing performance even for unknown individuals.

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Area of Science:

  • Affective Computing
  • Human-Computer Interaction
  • Social Signal Processing

Background:

  • Human emotion recognition relies on individual and interpersonal cues.
  • Existing automatic emotion recognition systems primarily use individual features.
  • The impact of interpersonal features, particularly synchronization, remains underexplored.

Purpose of the Study:

  • To investigate the benefit of interpersonal features, specifically time-lagged synchronization, for automatic emotion recognition.
  • To compare the performance of individual versus interpersonal frameworks in emotion recognition tasks.
  • To assess the generalizability of interpersonal features in speaker-dependent and speaker-independent scenarios.

Main Methods:

  • Developed and compared individual and interpersonal frameworks for emotion recognition.
  • Evaluated frameworks across visual, audio, and cross-modal data.
  • Conducted speaker-dependent and speaker-independent experiments.

Main Results:

  • The interpersonal framework significantly outperformed the individual framework in all tested modalities.
  • Improved performance with the interpersonal framework was observed even in speaker-independent settings with unknown communication pairs.
  • Time-lagged synchronization features proved beneficial for enhancing emotion recognition accuracy.

Conclusions:

  • Interpersonal features, especially time-lagged synchronization, are crucial for advancing automatic emotion recognition.
  • Incorporating interpersonal dynamics enhances the robustness and accuracy of emotion recognition systems.
  • Future research should prioritize the integration of interpersonal cues in affective computing.