A comprehensive approach to enhance emotion recognition through advanced feature extraction and Attention

Abstract

Emotion recognition from speech signals plays a critical role in various domains such as mental health evaluation and human–computer interaction. Traditional approaches often struggle to capture the intricate features and temporal relationships inherent in speech data, particularly in noisy environments. To address these limitations, this study introduces a novel hybrid model, termed CGAM (Capsule Networks Gated Recurrent Units and Attention Mechanism), which integrates Capsule Networks (CapsNet), Gated Recurrent Units (GRU), and an Attention Mechanism for robust speech and emotion recognition. The CGAM model leverages the hierarchical structure of CapsNet to extract layered features, while GRUs capture temporal dependencies in the data. The embedded Attention Mechanism enhances the model’s ability to focus on salient features, improving its discriminative power. Using the RAVDESS Emotional Speech Audio Dataset, the CGAM model achieves an accuracy of 98%, surpassing state-of-the-art methods in terms of accuracy, precision, recall, and F1-score. Ablation studies further validate the contributions of each component. This research offers a promising approach to advancing speech and emotion recognition systems, particularly in real-world, noisy environments, and lays the foundation for future applications in emotionally intelligent systems.