Emotion Recognition from Speech, Text, and Facial Expressions Using Meta-Learning

This paper presents a meta-learning methodology for emotion detection in multimedia content. The proposed approach uses three distinct models: Siamese network, convolutional neural network (CNN), and recurrent neural network (RNN) to identify emotions in a self-produced database of 1200 video and audio files. The Siamese network compares input files with reference to emotions to determine the emotion of the input file, while CNN classifies the input files into several emotion categories. In cases where data is scarce or insufficient to tackle a particular problem, meta-learning becomes a powerful strategy for quickly adapting to new scenarios and training with a limited set of examples. To enhance performance across various tasks, it is crucial to train a well-refined meta-learning model on a range of learning tasks. The justification for incorporating meta-learning in this study for emotion recognition lies in its versatility and adaptability, as it enables effective adaptation to novel environments and challenges. The performance of the models is evaluated against traditional machine learning methods, and the results show that the meta-learning approach achieves state-of-the-art performance on the self-created dataset. The suggested meta-learning strategy has the potential to enhance the precision and adaptability of current emotion detection methods. Additionally, it can be expanded to various domains and applications, including speech recognition and sentiment analysis.