Paper Key : IRJ************738
Author: Faraz Pathan,Swati Ghawate,Abdullaha Shaikh,Idris Shaikh,Huzaif Shaikh
Date Published: 12 Nov 2024
Abstract
The primary goal of this research is to create an AI-based video interview agent capable of predicting communication skills and personality traits by leveraging a multimodal analysis framework. Interviewing is essentialWe address the need for interactive platforms that combine AI-driven technologies with personalized feedback to evaluate communication skills and personality traits. By Investigating the application of machine learning (ML) models, including OpenCV, Wave2Vec, and BERT, we can determine their use in assessing communication skills and personality traits during job interviews. We explore how these ML models can objectively evaluate key interview metrics like candidate confidence, speech accuracy, and non-verbal cues, traditionally judged subjectively. Using a dataset of 500 recorded interviews, various techniques were applied to extract and analyze facial expressions, body language, and speech patterns. Results show that ML-based assessments correlate strongly with human evaluations, with an 85% agreement on confidence measures and 78% on answer correctness. These findings suggest that ML models, such as BERT for natural language processing (NLP) and Wave2Vec for speech recognition, can provide objective insights to complement human judgment in the hiring process, reducing bias and improving hiring outcomes.