AI-Based Virtual Interview Assistant: An Intelligent NLP-Driven System for Automated Interview Evaluation and Performance Analysis

Abstract: The rapid growth of competitive recruitment processes has increased the demand for intelligent interview prepa-ration platforms capable of providing personalized guidance and automated performance evaluation. Traditional mock interview systems depend heavily on trainers and manual evaluators, making the process time-consuming, inconsistent, and inaccessible to many students and job seekers. This paper presents an AI-Based Virtual Interview Assistant that automates interview simulation, response analysis, and performance assessment using Natural Language Processing (NLP) and speech processing techniques. The proposed system allows candidates to participate in domain- specific mock interviews using text or voice responses. Candidate answers are evaluated using semantic similarity analysis, TF-IDF vectorization, cosine similarity scoring, keyword matching, sentiment analysis, and communication assessment techniques. Speech responses are processed using Speech-to-Text conversion and analyzed for fluency, pauses, clarity, and confidence level. Based on the evaluation results, the system generates auto-mated feedback reports, performance scores, and improvement recommendations. The system was implemented using Python, Flask, PostgreSQL, spaCy, NLTK, HTML, CSS, JavaScript, and Tailwind CSS. Experimental evaluation demonstrates that the proposed system provides scalable, consistent, and intelligent interview preparation support while significantly reducing de-pendency on human evaluators.

Keywords: Artificial Intelligence, Natural Language Processing, Interview Assistant, Mock Interview System, Speech Analysis, Semantic Evaluation, Automated Feedback.