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Emotions are innate human responses significantly impacting cognition, behavior, and mental health. Accurate emotion recognition is essential for understanding how emotions affect individuals and developing new diagnostic and treatment methods. This study utilizes available data from the ICBHI Scientific Challenge to predict the emotional responses of nine levels and three types of videos. A dual CNN-LSTM model was proposed, incorporating multi-task learning with two parallel pathways to effectively integrate multi-task information. The experimental result lowered the external Phase I error to approximately 0.4, and achieved external Phase II error of 0.2944, demonstrating the potential of dual CNN-LSTM model for advancing emotion recognition and its applications in mental health.

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Advancing Emotional State Recognition Using Multi-task Learning with Dual CNN-LSTM

  • 会议论文

  • 发表日期：

    2025年

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  • Ching-Ping Wang,
  • Hsiang-Chin Chien,
  • Xin-Yu Chen,
  • Hong-Kun Lin,
  • Chi-Sheng Chang,
  • Chia-Yen Lee,
  • Jung-Chih Chen

摘要

Emotions are innate human responses significantly impacting cognition, behavior, and mental health. Accurate emotion recognition is essential for understanding how emotions affect individuals and developing new diagnostic and treatment methods. This study utilizes available data from the ICBHI Scientific Challenge to predict the emotional responses of nine levels and three types of videos. A dual CNN-LSTM model was proposed, incorporating multi-task learning with two parallel pathways to effectively integrate multi-task information. The experimental result lowered the external Phase I error to approximately 0.4, and achieved external Phase II error of 0.2944, demonstrating the potential of dual CNN-LSTM model for advancing emotion recognition and its applications in mental health.