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Related Concept Videos

Labeling Emotion01:20

Labeling Emotion

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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Multi-Modality Emotion Recognition Model with GAT-Based Multi-Head Inter-Modality Attention.

Changzeng Fu1,2, Chaoran Liu1, Carlos Toshinori Ishi1,3

  • 1Advanced Telecommunications Research Institute International, Kyoto 619-0288, Japan.

Sensors (Basel, Switzerland)
|September 3, 2020
PubMed
Summary
This summary is machine-generated.

This study enhances artificial agent emotion recognition using multi-modal data fusion. A novel approach combining audio augmentation and a graph attention network (GAT) improved accuracy and F1-scores.

Keywords:
emotion recognitiongraph attention networkmulti-modality

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Affective Computing

Background:

  • Emotion recognition is crucial for artificial agents.
  • Multi-modality models leverage diverse data strengths.
  • Effective fusion of multi-modal data remains a challenge.

Purpose of the Study:

  • To investigate optimal fusion strategies for multi-modal emotion recognition.
  • To improve the performance of emotion recognition systems for artificial agents.

Main Methods:

  • Proposed audio sample augmentation technique.
  • Developed an emotion-oriented encoder-decoder architecture.
  • Implemented an inter-modality, decision-level fusion using a Graph Attention Network (GAT).

Main Results:

  • Achieved weighted average F1-scores of 68.31%, an improvement from 64.18%.
  • Increased weighted average accuracy to 69.88%, up from 65.25%.
  • Demonstrated the effectiveness of the proposed fusion method over baseline models.

Conclusions:

  • The proposed fusion method, incorporating audio augmentation and GAT, significantly enhances multi-modal emotion recognition.
  • This research provides a viable approach for more sophisticated emotion recognition in artificial agents.