Multimodal Stress Detection Using Facial Expressions and Voice Analysis

This Research suggests a multimodal framework for stress detection by combining voice and face analysis and utilising advances in machine learning and signal processing, while speech characteristics reflect auditory alterations associated with stress, facial expressions offer important visual indicators. When both modalities are combined, the suggested solution outperforms unimodal methods in terms of accuracy and dependability. This approach combine the outputs of recurrent neural networks (RNNs) for voice feature extraction and convolutional neural networks (CNNs) for facial analysis to classify stress. Applications for this approach in personal wellness, business stress monitoring, and healthcare have a lot of promise.