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)]
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Journal of Mechatronics and Automation
Volume | 11 |
Issue | 02 |
Received | May 27, 2024 |
Accepted | June 4, 2024 |
Published | August 31, 2024 |