Emotion Analysis from Face and Speech: A Comprehensive Review of Current Techniques and Models

Emotion detection in facial expressions and speech plays a crucial role in enhancing interactive platforms, particularly in learning and assessment systems. This study explores advanced techniques for integrating dynamic mock test generation and interview simulation modules in an Advanced Placement Preparation Platform. The dynamic mock test uses real-time feedback to recommend questions based on a student’s performance, leveraging machine learning algorithms for adaptive learning. Additionally, the interview simulation module incorporates facial expression recognition using Convolutional Neural Networks (CNNs) and speech analysis using Recurrent Neural Networks (RNNs) to evaluate student performance. Initial results show that traditional models struggle with real-time adaptability and emotion classification accuracy, underscoring the need for specialized algorithms for complex data. To address these limitations, the system evaluates deep learning models designed for adaptive learning and emotion analysis, such as transfer learning models in emotion detection. The findings highlight the potential for using multimodal data to improve user engagement and performance evaluation in educational settings, paving the way for more immersive and intelligent learning platforms.