International Research Journal of Education and Technology

Embodiment is very essential in the expression and management of emotions at work, especially since the physical expressions of stress, such as facial expressions and voice changes, are crucial indicators of the emotional status of workers. The understanding of these embodied expressions enhances the emotional intelligence of teams because of better communication and effective conflict resolution. This paper examines how machine learning and deep learning have approached the problem of stress detection through facial expression and vocal attributes with their dependence on convolutional neural networks for efficient identification of minor indicators, which might include furrowed eyebrows and the presence of elements in a voice like pitch and tone. Datasets like FER2013 and RAVDESS make data augmentation techniques integrate model robustness, and a more precise time analysis through neural networks improves the real-time capability of the model. Also, a multimodal fusion of facial and vocal features further helps to enhance Embodiment is very essential in the expression and management of emotions at work, especially since the physical expressions of stress, such as facial expressions and voice changes, are crucial indicators of the emotional status of workers. The understanding of these embodied expressions enhances the emotional intelligence of teams because of better communication and effective conflict resolution. This paper examines how machine learning and deep learning have approached the problem of stress detection through facial expression and vocal attributes with their dependence on convolutional neural networks for efficient identification of minor indicators, which might include furrowed eyebrows and the presence of elements in a voice like pitch and tone. Datasets like FER2013 and RAVDESS make data augmentation techniques integrate model robustness, and a more precise time analysis through neural networks Also, a multimodal fusion of facial and vocal features further helps to enhance overall accuracy in relation to the level of perception about stress levels. It would help organizations be proactive about addressing emotional health and therefore lead to a healthier workplace environment and the well-being of employees. Overall accuracy in relation to the level of perception about stress levels. It would help organizations be proactive about addressing emotional health and therefore lead to a healthier workplace environment and the well-being of employees.

Keywords: Stress detection, CNN, facial expressions, vocal analysis, deep learning, RAVDESS, FER2013, emotion recognition.