AI-Based Classroom Lecture Analyzer and ERP System Using Speech Recognition and Natural Language Processing

Author

Korva Srikar, Dr. V. Harsha Shastri

Abstract

 Classroom teaching generates a large amount of spoken information that students are expected to understand and record through manual note-taking. During fast-paced lectures, students may miss important concepts, and those who are absent may have limited access to the content discussed in class. Teachers also require efficient methods for maintaining digital lecture records and monitoring academic progress.

This paper presents an AI-Based Classroom Lecture Analyzer and ERP System that combines Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and academic management functionalities in a unified platform. The system converts classroom speech into text, processes lecture transcripts to identify important keywords and topics, and generates concise summaries and structured PDF notes. It also provides centralized lecture storage and role-based dashboards for Students, Teachers, and Administrators.

The proposed system further supports syllabus completion tracking and lecture effectiveness analysis to assist academic monitoring. By integrating lecture processing with ERP functionality, the system aims to reduce manual note-taking, improve access to missed lecture content, and provide an organized platform for managing classroom information and academic activities.

Keywords

Artificial Intelligence, Natural Language Processing, Speech Recognition, Automatic Speech Recognition, Classroom Lecture Analysis, Speech-to-Text,

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References

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