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This study shows a better version of a sentiment analysis system that uses different types of body signals with the help of smart AI and deep learning methods. It uses the CMU-MOSEI dataset and mixes face expressions, voice features, and text meaning to understand emotions more deeply. A Modified Cross-Modality Transformer (M-CMT) is used for audio by working with Mel spectrograms. ResNet50 is used to take features from face images, and BERT is used for the text part, which is also a transformer-based model. In this system, a self-attention layer helps to understand better how the features from the same type of input are connected, and cross-attention is used to bring together all the different inputs for more correct emotion recognition. The main goal of this system is to help watch over mental health. Finding emotional problems early can help give treatment on time, and also make the treatment work better. This work helps move forward the field of smart systems that can understand emotions.

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Multimodal Biometric Emotion Recognition for Mental Health Monitoring Using AI-DL Techniques

  • 章节

  • 发表日期：

    2026年

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  • Sk Md Hasnat,
  • Tilendra Shishir Sinha

摘要

This study shows a better version of a sentiment analysis system that uses different types of body signals with the help of smart AI and deep learning methods. It uses the CMU-MOSEI dataset and mixes face expressions, voice features, and text meaning to understand emotions more deeply. A Modified Cross-Modality Transformer (M-CMT) is used for audio by working with Mel spectrograms. ResNet50 is used to take features from face images, and BERT is used for the text part, which is also a transformer-based model. In this system, a self-attention layer helps to understand better how the features from the same type of input are connected, and cross-attention is used to bring together all the different inputs for more correct emotion recognition. The main goal of this system is to help watch over mental health. Finding emotional problems early can help give treatment on time, and also make the treatment work better. This work helps move forward the field of smart systems that can understand emotions.