By
Gayatri Ganeshkar,
Nikita Gawade,
Damayanti Badadhe,
Hema Deshmukh,
N. M. Dimble,
- Student Department of Computer Engineering, RDTC’s Shri Chhatrapati Shivajiraje College of Engineering, Dhangawadi Maharashtra India
- Student Department of Computer Engineering, RDTC’s Shri Chhatrapati Shivajiraje College of Engineering, Dhangawadi Maharashtra India
- Student Department of Computer Engineering, RDTC’s Shri Chhatrapati Shivajiraje College of Engineering, Dhangawadi Maharashtra India
- Student Department of Computer Engineering, RDTC’s Shri Chhatrapati Shivajiraje College of Engineering, Dhangawadi Maharashtra India
- Student Department of Computer Engineering, RDTC’s Shri Chhatrapati Shivajiraje College of Engineering, Dhangawadi Maharashtra India
Abstract
In the contemporary era of virtual interviews, the need for a comprehensive system to prepare users for online interviews is imperative. Mock interviews serve as invaluable tools for enhancing confidence and communication skills, ultimately improving performance. This paper introduces a groundbreaking AI-Driven Mock Interview System (MIS) fortified with cutting-edge Natural Language Processing (NLP) methodologies, specifically targeting syntax and semantic analysis. The MIS integrates a robust JSON-based question- answer repository spanning diverse interview scenarios, coupled with sophisticated text-to-speech capabilities for audible question delivery, ensuring an immersive interview experience. Upon receipt of user responses in audio format, the MIS employs advanced NLP algorithms for speech-to-text conversion and subsequent syntactic and semantic analysis. Future research may explore further refinements and applications of AI- driven technologies to continuously optimize virtual interview experiences, empowering candidates globally
Keywords: Syntax and semantic analysis, NLP (natural language processing), virtual interviews, JSON.
[This article belongs to Journal of Mechatronics and Automation(joma)]
How to cite this article: Gayatri Ganeshkar, Nikita Gawade, Damayanti Badadhe, Hema Deshmukh, N. M. Dimble. Enhancing Interview Preparedness: Development of A Comprehensive AI-Driven Mock Interview System. Journal of Mechatronics and Automation. 2024; 11(02):19-25.
How to cite this URL: Gayatri Ganeshkar, Nikita Gawade, Damayanti Badadhe, Hema Deshmukh, N. M. Dimble. Enhancing Interview Preparedness: Development of A Comprehensive AI-Driven Mock Interview System. Journal of Mechatronics and Automation. 2024; 11(02):19-25. Available from: https://journals.stmjournals.com/joma/article=2024/view=169991
References
- P. SWETHA, 2B.GOWTHAMI, 3CH.SREEMAN, 4G. AMRUTH. AI -Based mock interview evaluator: An emotion and confidence classifier model. Volume IX Issue I APRIL 2024. ZKG International.
- Yi-Chi Chou, Felicia R. Wongso, Chun- Yen Chao and Han-Yen Yu, “An AI Mock- interview Platform for Interview Performance Analysis”, 10th International Conference on Information and Education Technology, 2022, Japan.
- Aditi S. More, Samiksha S. Mobarkar, Siddhita S.Salunkhe, Reshma R. Chaudhari,”SMART INTERVIEW USING AI” 2022. VOLUME-9, ISSUE-1. Technical Research Organization India. 7-11.
- “Danai Styliani Moschona,” An Affective Service based on Multi-Modal Emo on Recognition, using EEG enabled Emotion Tracking and Speech Emotion Recognition”, 2020 IEEE International Conference on Consumer Electronics – Asia (ICCE-Asia). Seoul, Korea (South)
- Luis Felipe Parra Gallegoa, Juan Rafael Orozco-Arroyave, “Classification of emotions and evaluation of customer satisfaction from speech in real-world acoustic environments”, . Digital Signal Processing. Volume 120, January 2022, 103286
- Xinpei Jin1,2, Yulong Bian*1,2, Wenxiu Geng1,2, Yeqing Chen1,2, Ke Chu1,2, Hao Hu1,2, Juan Liu1,2, Yuliang Shi1,3, and Chenglei Yang Xinpei Jin, Yulong Bian, Wenxiu Geng, Yeqing Chen, Ke Chu, Hao Hu, Juan Liu, Yuliang Shi, and Chenglei Yang, “Developing an Agent- based Virtual Interview Training System for College Students with High Shyness Level”, IEEE 2019. Japan. IEEE Conference on Virtual Reality and 3D User Interfaces
- C. Chou and H. Y. Yu, “Interview Based on the application of AI technology in resume analysis and job recommendation”, IEEE International ConferenceonComputational Electromagnetics (ICCEM), pp. 291-296, 2020.
- Vikash Salvi, Adnan Vasanwalla, Niriksha Aute and Abhijit Joshi,”Virtual Simulation of Technical Interviews”, IEEE 2017. International Conference on Computing, Communication, Control and Automation (ICCUBEA). Pune, India
- Dulmini Yashodha Dissanayake, Venuri Amalya, Raveen Dissanayaka3, Lahiru Lakshan, Pradeepa Samarasinghe, Madhuka Nadeeshani, Prasad Samarasinghe, “Al-based Behavioural Analyser for Interviews/Viva” IEEE 16th International Conference on Industrial and Information Systems (ICIIS),2021.
- Ming-Hsiang Su, Kun-Yi Huang, Tsung-Hsien Yang, Kuan-Jung Lai and Chung-Hsien Wu ” DIALOG STATE TRACKING AND ACTION SELECTION USINGDEEPLEARNING MECHANISM FOR INTERVIEW COACHING”. Proceedings of the 2016 International Conference on Asian Language Processing, IALP. Taiwan
- Hung-Yue Suen1, Kuo-En Hung1, and Chien-Liang Lin “Tensor Flow-based Automatic Personality Recognition Used in Asynchronous Video Interviews”. IEEE Volume 7. 2019. 61018 – 61023
- “Hongli Zhang, Alireza Jolfaei, And Mamoun Alazab “A Face Emotion Recognition Method Using Convolutional Neural Network and Image Edge Computing”. IEEE. 159081-159089. 2019.
Regular Issue Subscription Original Research
Journal of Mechatronics and Automation
ISSN: 2455-1988
| Volume | 11 |
| Issue | 02 |
| Received | May 27, 2024 |
| Accepted | June 4, 2024 |
| Published | August 31, 2024 |