Multimodal Ai Framework for Interview Assessment Using Facial Expression and Speech Signals

The rapid advancement of artificial intelligence has enabled automated solutions to enhance recruitment processes. This study proposes a smart interview evaluation system that integrates facial expression recognition and voice analysis to provide objective and data-driven insights into candidate performance. The system utilizes computer vision techniques to detect facial emotions, micro-expressions, and non-verbal cues, while speech processing algorithms analyze tone, pitch, articulation, and linguistic features to assess confidence, clarity, and emotional state. By combining multimodal data, the framework delivers a comprehensive evaluation of communication skills and behavioral patterns. Machine learning models trained on diverse datasets improve adaptability across different cultural and linguistic contexts. Experimental results demonstrate that the system achieves reliable performance in identifying candidate traits, reducing human bias, and improving decision-making accuracy. This approach enhances efficiency, fairness, and consistency, making it a valuable tool for modern recruitment systems.