Enhanced Speech Emotion Recognition Using Hybrid Machine Learning and Deep Learning Models

Abstract - In recent times, recognizing human emotions accurately has become crucial for enhancing humancomputer interaction. Speech Emotion Recognition (SER) enables systems to interpret emotional states from speech signals, improving applications such as virtual assistants, mental health monitoring, and affective computing. However, accurately classifying emotions remains a challenge due to the complexity of speech variations. In this paper, we propose a hybrid approach that integrates traditional machine learning techniques with deep learning models to improve emotion classification.