Abstract
Abstract: Emotion recognition plays a vital role in various applications, such as human-computer interaction, affective computing, and psychological assessment. This comparative study investigates the effectiveness of emotion recognition through text, image, and speech modalities. Our research aims to analyze and compare state-of-art approaches in each modality and identify their strengths and limitations. Conducting an exhaustive literature review enabled us to understand existing methodologies, datasets, and evaluation metrics. The research methodology and implementation include data collection, preprocessing, feature extraction, and the application of machine learning and deep learning models. The results provide insights into the performance of different modalities, paving the way for advancements in emotion recognition research.
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