An AI-Driven Approach to Enhance Interview Performance through Voice and Response Analysis

Abstract

In today’s competitive job market, interview prepa ration is a daunting issue for candidates due to a lack of proper and personal practice opportunities. The conventional approach through self-practice and generic mock interviews cannot ensure an immersive and personalized experience, leaving candidates poorly prepared and nervous. To solve this problem, this pa per suggests an AI-based virtual interview system, delivering immersive, role-playing mock interviews that are customized to meet the requirements of different jobs. The system involves dynamic question selection, speech fluency assessment, confidence evaluation via facial emotion recognition, and answer relevance assessment via natural language processing methods. Questions are dynamically chosen according to job descriptions, skills, and levels of experience to provide a systematic and varied inter view process. Fluency in speech is evaluated through hesitation patterns, filler words, and pause detection, while confidence measurement is carried out in real-time using facial expression analysis based on a convolutional neural network . Relevance of the answers is determined based on Cosine Similarity as well as BERT , and the results have been shown to offer higher accuracy for evaluating contextual meaning with BERT. The candidates are given information about their strengths and weaknesses, allowing them to improve their answers, increase confidence, and enhance communication overall. Through the imitation of actual interview situations, the system is an all-around resource for job applicants, increasing their likelihood of success in business interviews.