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Linking emotions to behaviors through deep transfer learning.

Haoqi Li1, Brian Baucom2, Panayiotis Georgiou1

  • 1Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, United States of America.

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This study uses deep learning to analyze how emotions influence human behavior during conversations. Findings show that emotional cues are vital for recognizing behavior, and the sequence of emotions is critical.

Keywords:
Affective computingBehavior quantificationCouples therapyEmotionNeural networks

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Area of Science:

  • Computational Linguistics
  • Affective Computing
  • Behavioral Psychology

Background:

  • Understanding human behavior is crucial in fields like psychotherapy.
  • Quantifying the nonlinear relationship between emotions and behavior is challenging.
  • Emotional dynamics during conversations offer key insights into behavior.

Purpose of the Study:

  • To investigate the inferential capacity and contextual importance of emotions in behavior recognition.
  • To develop a deep transfer learning framework for quantifying behavior based on emotional cues.
  • To explore the significance of emotional context and sequence in behavior expression.

Main Methods:

  • Employed deep transfer learning, combining convolutional and recurrent neural networks.
  • Trained a network to quantify emotions from acoustic signals.
  • Utilized emotion recognition outputs as features for behavior recognition, treating them as behavioral primitives.

Main Results:

  • Emotion-related information was identified as a significant cue for behavior recognition.
  • The sequence of emotions was found to be critical for behavior expression.
  • Constraining the neural network's contextual view highlighted the importance of emotional context.

Conclusions:

  • Deep transfer learning effectively quantifies human behavior by integrating emotional dynamics.
  • Emotional sequence and context are indispensable factors in understanding and recognizing behavior from speech.
  • The developed hybrid architectures facilitate automatic behavior recognition from acoustic and emotional data.