Adaptive Virtual Assistant Interaction through Real-Time Speech Emotion Analysis Using Hybrid Deep Learning Models and Contextual Awareness

Abstract

The integration of real-time speech emotion analysis with contextual awareness in virtual assistants has the potential to significantly enhance user interactions. This study presents a novel approach to adaptive virtual assistant interaction by employing hybrid deep learning models, specifically 1D Convolutional Neural Networks (CNNs) combined with attention mechanisms, to accurately detect and interpret user emotions. Additionally, the system incorporates contextual awareness, leveraging conversation history, user preferences, and environmental factors to adapt responses dynamically. The hybrid model is trained on a comprehensive speech emotion dataset, and its performance is evaluated against baseline methods using various metrics, including accuracy, precision, recall, and F1-score. Comparative analyses and ablation studies highlight the impact of the attention mechanisms and contextual modules. Real-time performance tests demonstrate the system's responsiveness and efficiency in a simulated virtual assistant environment. The integration of the emotion recognition system into a virtual assistant framework is detailed, with examples of adaptive interaction scenarios. A user experience study assesses the impact on user satisfaction and interaction quality. The findings indicate significant improvements in the virtual assistant's ability to respond appropriately to user emotions and contexts, paving the way for more personalized and engaging user experiences. Future research directions include exploring multimodal emotion recognition and further enhancing system robustness.