AI Based Mock Interview System Using Natural Language Processing

Mock interviews are crucial for job applicants to enhance and polish their skills before to real interviews. This study presents a novel AI-driven Mock Interview System that replicates authentic interview settings through the utilization of Natural Language Processing (NLP), Deep Learning, and Computer Vision. The system incorporates Speech-to-Text for response processing, Text-to-Speech for feedback dissemination, and Reinforcement Learning (RL) for the adaptive assessment of response precision. Employing OpenCV and MediaPipe, it examines behaviors such as facial expressions, ocular movements, and additional non-verbal signals. The system produces domain-specific questions, assesses fluency, coherence, and content, and delivers comprehensive feedback on linguistic skills, technical knowledge, and professional conduct. Experimental findings indicate 95% accuracy in verbal communication analysis, 93% precision in behavioral assessment, and a 90% user satisfaction rating. The solution transcends conventional mock interview constraints, guaranteeing scalability, personalization, and immediate feedback, thereby enabling job seekers to refine their skills and bolster their confidence for successful interviews.