Multimodal Emotion Recognition: An Integrated Approach using Facial, Audio and Text Analysis

In an era among the increasing interaction between humans and technology, the capacity to accurately recognize and the ability to react to human emotions has become crucial particularly in fields such as medical care, customer support, and personal assistance, where empathetic responses can greatly enhance user experience and effectiveness. This study presents an integrated emotion recognition system that leverages various modalities, including facial expressions, audio, and text, to determine a user's emotional state. The system processes input data from diverse sources—such as the Toronto Emotion Speech Set (TESS) and FER datasets—through an architecture involving data cleaning, preprocessing, and feature extraction. The proposed approach utilizes MobileNetV2 for facial analysis using Convolutional Neural Networks (CNN) with the FER dataset, a Multilayer Perceptron (MLP) with Librosa for audio analysis using the TESS dataset, and Natural Language Processing (NLP) combined with Linear Regression for text analysis. Data preprocessing ensures the caliber and reliability after which the input data is divided into training and testing and testing datasets. Tranining of the model is done using a mix of MobileNetV2, MLP, and NLP algorithms, with the final model employing an ensemble-based approach to combine the outputs of facial, audio, and textual analyses into a unified emotion prediction. A compilation of the model is followed by training and evaluation, optimizing the model’s performance by saving the weights and parameters. The trained model receives real-time inputs—facial images, audio clips, or text messages— processed through a user-friendly interface to recognize emotions effectively. The study aims to create an efficient and accessible solution to real-time emotion detection, integrating machine learning models to enhance user interaction.