A Multimodel Approach to Recognize Emotion Using Deep Learning Techniques

Abstract

Emotion recognizing human emotions has become an important research topic as it supports various domains including interactive systems, medical support tools, safety applications, and intelligent services. While traditional systems focus on a single input source, such as text, speech, or facial expressions, relying on one modality often limits accuracy in real-world scenarios. In this study, we propose an integrated approach that merges sentiment identification from text, voice-based emotion interpretation, and visual expression analysis through advanced deep learning models. The proposed system processes user text using an NLP-based model, processes audio signals by extracting key acoustic patterns like MFCC, and determines facial emotions using a CNN trained on expression datasets. By merging outputs generated by the three independent modules, the system produces a more reliable and consistent emotion output. Experimental evaluation shows that the multimodel architecture outperforms individual models, demonstrating improved accuracy and stability, especially in situations involving noise, ambiguous text, or partial visibility. This approach highlights the potential of deep learning driven multimodal emotion detection to build more smart and responsive intelligent systems.