AI-Powered Mock Interview Platform using Computer Vision, Natural Language Processing and Generative AI

In this paper, we propose an AI-based mock interview platform addressing effective interviewing techniques with real-time results and intelligent mock interviews for candidates. It is a combination of generative AI, computer vision, and natural language processing (NLP) that provides a simulated experience of real-time interviews. It still continues to use methods such as CNNs for Facial expressions analysis, emotion detection from facial expressions and Voice recognition and NLP for tone, fluency, and confidence detection of a candidate. The platform compares responses with relevant industry standards using keyword-based semantic analysis in order to evaluate domain knowledge. The main highlight of this work includes emotion and answer-based feedback, where it provides the candidate with a performance report and suggestions for improvement. This ensures that users receive dynamic, multimodal insights that are tailored to the user profile (job role and skill set) and go beyond the scope of traditional mock interviews, where all interview questions feel the same. Overall, the platform is designed to help minimize stress, enhance communication capabilities, and empower career progression, while contributing to the evolution of AI in talent development and acquisition.