A REVIEW ON AI-POWERED SMARTINTERVIEW SYSTEM USING BERT-CNN FOR SEMANTIC ANSWER
AUTHORS:
Mentor
Prof. Walke P.R
Affiliation
Department of Computer Engineering Vidya Niketan College of Engineering,
Bota Pune-Nashik Highway Maharashtra, India.
Bota Pune-Nashik Highway Maharashtra, India.
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Abstract
The proposed AI Interview Assistant with Voice, Emotion and Skill Analysis is an intelligent Android application developed to help candidates prepare for job interviews through automated assessment and personalized feedback. Traditional interview preparation methods generally depend on self-practice, coaching, or guidance from experienced individuals, which may not provide continuous performance evaluation. To overcome these limitations, the proposed system combines Artificial Intelligence, Machine Learning, Computer Vision, Speech Processing and Natural Language Processing to analyse different aspects of interview performance. A CNN-based model is used to identify facial expressions, while MFCC-based feature extraction and machine-learning techniques are applied to analyse voice characteristics and speech emotions. Speech-to-text technology converts spoken responses into text, which is further processed using NLP techniques to examine answer relevance, important keywords, communication quality and technical skill alignment. The results obtained from facial, voice and textual analysis are combined to generate an overall performance report. The system also provides personalized recommendations, interview improvement tips, learning resources and skill development suggestions. Firebase is used to maintain interview history and monitor performance over multiple practice sessions. The proposed application aims to provide an accessible and interactive interview preparation platform that supports candidates in improving their communication, technical knowledge and behavioural presentation skills.
Keywords
Artificial Intelligence Machine Learning Android Application Interview Assessment Facial Emotion Recognition Convolutional Neural Network Speech Emotion Recognition MFCC Natural Language Processing Speech-to-Text Multimodal Analysis Skill Assessment Recommendation System.
Article Information
PUBLICATION DETAILS
Review Type:
Double-Blind Peer Review
License:
CC BY-NC 4.0
Volume/Issue:
Volume 02, Issue 11
Review Rounds:
2
Article Type:
Research Article
Publication Date:
Nov 05 2026
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Nathe, C. N., Thore, G. V., Sonavane, M. P. & Karade, S. S. (2026). A Review on AI-Powered SmartInterview System Using BERT-CNN for Semantic Answer. International Journal of Science, Strategic Management and Technology, 02(11), 1-9. https://doi.org/10.55041/ijsmt.v2i11.003
Nathe, Chaitali, et al.. "A Review on AI-Powered SmartInterview System Using BERT-CNN for Semantic Answer." International Journal of Science, Strategic Management and Technology, vol. 02, no. 11, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijsmt.v2i11.003.
Nathe, Chaitali,Gaurav Thore,Manoj Sonavane, and Suraj Karade. "A Review on AI-Powered SmartInterview System Using BERT-CNN for Semantic Answer." International Journal of Science, Strategic Management and Technology 02, no. 11 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijsmt.v2i11.003.
References
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[4] E. del Gobbo, A. Guarino, B. Cafarelli, and L. Grilli, "GradeAid: A framework for automatic short answers grading in educational contexts—design, implementation and evaluation," Knowledge and Information Systems, vol. 65, pp. 4295–4334, 2023.
[5] Y. Gan, L. Xu, H. Xia, and colleagues, "Harmonious mutual learning for facial emotion recognition," Neural Processing Letters, vol. 56, Art. no. 96, 2024.
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[7] J.-Y. Kuo, T.-F. Hsieh, and T.-Y. Lin, "Constructing multi-modal emotion recognition model based on convolutional neural network," Multimedia Tools and Applications, vol. 84, pp. 31093–31118, 2025.
[8] C. Gupta, N. S. Gill, P. Gulia, A. Kumar, H. Karamti, D. M. Moges, and colleagues, "A multimodal fusion model for real-time environment emotion recognition using audio-visual-textual features," Journal of Big Data, vol. 12, Art. no. 256, 2025.
[9] Y. Li, Y. Wang, X. Yang, and colleagues, "Speech emotion recognition based on Graph-LSTM neural network," EURASIP Journal on Audio, Speech, and Music Processing, vol. 2023, Art. no. 40, 2023.
[10] J. M. Basch and K. G. Melchers, "Here's looking at you: Does eye contact in video interviews affect how applicants are perceived and evaluated?" Journal of Business and Psychology, vol. 40, pp. 669–685, 2025.
[2] T. Liu and X. Yuan, "Paralinguistic and spectral feature extraction for speech emotion classification using machine learning techniques," EURASIP Journal on Audio, Speech, and Music Processing, vol. 2023, Art. no. 23, 2023.
[3] F. Stoehr, B. Kämpgen, L. Müller, L. Oleaga Zufiría, V. Junquero, C. Merino, P. Mildenberger, and R. Kloeckner, "Natural language processing for automatic evaluation of free-text answers – a feasibility study based on the European Diploma in Radiology examination," Insights into Imaging, vol. 14, Art. no. 150, 2023.
[4] E. del Gobbo, A. Guarino, B. Cafarelli, and L. Grilli, "GradeAid: A framework for automatic short answers grading in educational contexts—design, implementation and evaluation," Knowledge and Information Systems, vol. 65, pp. 4295–4334, 2023.
[5] Y. Gan, L. Xu, H. Xia, and colleagues, "Harmonious mutual learning for facial emotion recognition," Neural Processing Letters, vol. 56, Art. no. 96, 2024.
[6] R. Karani, J. Jani, and S. Desai, "FER-BHARAT: A lightweight deep learning network for efficient unimodal facial emotion recognition in Indian context," Discover Artificial Intelligence, vol. 4, Art. no. 35, 2024.
[7] J.-Y. Kuo, T.-F. Hsieh, and T.-Y. Lin, "Constructing multi-modal emotion recognition model based on convolutional neural network," Multimedia Tools and Applications, vol. 84, pp. 31093–31118, 2025.
[8] C. Gupta, N. S. Gill, P. Gulia, A. Kumar, H. Karamti, D. M. Moges, and colleagues, "A multimodal fusion model for real-time environment emotion recognition using audio-visual-textual features," Journal of Big Data, vol. 12, Art. no. 256, 2025.
[9] Y. Li, Y. Wang, X. Yang, and colleagues, "Speech emotion recognition based on Graph-LSTM neural network," EURASIP Journal on Audio, Speech, and Music Processing, vol. 2023, Art. no. 40, 2023.
[10] J. M. Basch and K. G. Melchers, "Here's looking at you: Does eye contact in video interviews affect how applicants are perceived and evaluated?" Journal of Business and Psychology, vol. 40, pp. 669–685, 2025.
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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
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