An Speech-Based Emotional Artificial Intelligence Detection Framework

The ability to recognize emotions in speech-based applications represents a significant advancement in human-computer interaction, offering a more intuitive and empathetic interface between users and technology. This paper presents a framework for speech-based emotional artificial intelligence detection utilizing an Efficient Capsule Network (CapsNet) model, trained and validated with the CREMA-D dataset. The Efficient-CapsNet model's unique architecture excels in capturing the intricate spatial relationships within speech data, resulting in superior accuracy and robustness compared to traditional models. This enhanced capability enables real-time, nuanced emotion detection, making it applicable in diverse domains such as mental health monitoring, customer service, and interactive entertainment. The use of the CREMA-D dataset ensures the model is well-equipped to handle a wide range of emotional expressions, facilitating generalization across varied real-world scenarios. The findings demonstrate the model's efficiency in terms of computational load and processing speed, highlighting its suitability for deployment in real-time applications. This research underscores the importance of emotion recognition in improving user experiences, providing timely mental health support, and creating more engaging and empathetic interactions in technology-driven environments. Future work will focus on optimizing the model, expanding its emotional detection capabilities, and ensuring ethical standards are upheld in its deployment.