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Avishek Das1, Moumita Sen Sarma1, Mohammed Moshiul Hoque1

  • 1Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong 4349, Bangladesh.

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Summary
This summary is machine-generated.

Researchers developed a new multimodal Bangla dataset and framework for emotion recognition, improving accuracy by integrating audio, video, and text data.

Keywords:
cross-modal attentionmultimodal datasetmultimodal emotion recognitionnatural language processingtransformers

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Multimodal emotion classification (MEC) integrates audio, video, and text for robust emotion recognition.
  • Challenges include fusing diverse data modalities and the lack of Bangla-specific datasets.
  • Existing systems struggle with nuanced emotional expression in Bangla.

Purpose of the Study:

  • To introduce the MAViT-Bangla dataset, a novel multimodal resource for Bangla emotion recognition.
  • To develop and evaluate a cross-modal attention framework (AVaTER) for enhanced MEC.
  • To address the limitations of unimodal approaches in Bangla emotion analysis.

Main Methods:

  • Created MAViT-Bangla dataset with 1002 audio, video, and text samples covering anger, fear, joy, and sadness.
  • Developed the AVaTER framework utilizing cross-modal attention for feature fusion.
  • Evaluated the framework's performance against unimodal methods.

Main Results:

  • The MAViT-Bangla dataset provides a comprehensive resource for Bangla MEC research.
  • The AVaTER framework achieved an F1-score of 0.64.
  • This represents a significant improvement over unimodal emotion recognition techniques.

Conclusions:

  • The MAViT-Bangla dataset is a valuable contribution to multimodal emotion recognition research in Bangla.
  • The AVaTER framework effectively integrates multimodal features for improved emotion classification accuracy.
  • Future work can leverage this dataset and framework for more sophisticated Bangla emotion understanding.