Year: 2025
Type: article
Abstract: In today’s highly competitive job market, interview preparedness has become more critical than ever. Traditional mock interviews often lack scalability, objectivity, and actionable insights. To address these limitations, this paper proposes an AI-Based Mock Interview Evaluator that combines emotion recognition and confidence classification to assess candidate performance. Using facial expression analysis and speech feature extraction, the system offers real-time feedback on emotional state and confidence level during simulated interview sessions. The model leverages Convolutional Neural Networks (CNNs) for emotion detection and supervised machine learning techniques for vocal confidence estimation. Experimental results show that the system can classify emotional and confidence cues with high accuracy, providing an interactive and personalized experience for users. The proposed tool aims to democratize interview training by offering intelligent, automated feedback that enhances self-awareness and performance for job seekers. This paper presents an AI-driven mock interview evaluator designed to assess candidates' emotional states and confidence levels during simulated interviews. By integrating facial expression recognition, speech analysis, and machine learning classifiers, the system provides real-time feedback to enhance interview preparation. The model aims to bridge the gap between traditional interview coaching and modern AI capabilities, offering a scalable solution for personalized interview training.
Authors: Neha Gupta, Aashi Sharma, Suneet Shukla, Praveen Kumar
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
SDG: Quality education
Open Access status: diamond
OpenAlex ID: W4411291071