Smart System for Generating Lecture Notes from Audio using Artificial Intelligence

The AI-based Lecture Notes Generation System provides an integrated approach for converting recorded lecture audio into structured digital learning material. The system combines audio preprocessing, Whisper-based speech recognition, NLP-based transcript processing, RAKE keyword extraction, transformer-based summarization, structured note generation, and question answering. The modular Flask-based backend coordinates the processing stages, while HTML, CSS, and JavaScript provide the user-facing interface. The main usefulness of the proposed system is its ability to connect speech recognition with downstream language-processing functions rather than stopping at transcription. The resulting representation can support revision by presenting a transcript, important keywords, a concise summary, structured notes, and content-related questions and answers. The system therefore provides a practical foundation for AI-assisted educational content processing. The current work has several limitations. Speech-recognition quality may vary with noise, accents, overlapping speech, and recording conditions. Long recordings can require substantial processing time. Abstractive summaries may omit details or require verification against the source transcript. In addition, the exact T5-family checkpoint used in the final implementation must be verified because the project description and project presentation identify different model variants. Future development can extend the system toward multilingual speech support, real-time lecture transcription, speaker identification, improved summarization, retrieval-grounded question answering, cloud deployment, mobile applications, and personalized learning features. Future experiments should also establish a representative evaluation dataset and report WER, summarization metrics, keyword-extraction metrics, QA performance, processing time, and user evaluation.

[1] G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, B. Kingsbury, and T. N. Sainath, “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” IEEE Signal Process. Mag., vol. 29, no. 6, pp. 82–97, Nov. 2012, doi: 10.1109/MSP.2012.2205597. [2] R. Prabhavalkar, T. Hori, T. N. Sainath, R. Schlüter, and S. Watanabe, “End-to-end speech recognition: A survey,” IEEE/ACM Trans. Audio, Speech, Language Process., vol. 32, pp. 325–351, 2024, doi: 10.1109/TASLP.2023.3328283. [3] A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervision,” arXiv:2212.04356, 2022. [4] S. Rose, D. Engel, N. Cramer, and W. Cowley, “Automatic keyword extraction from individual documents,” in Text Mining: Applications and Theory. Chichester, U.K.: Wiley, 2010, pp. 1–20, doi: 10.1002/9780470689646.ch1. [5] C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” J. Mach. Learn. Res., vol. 21, no. 140, pp. 1–67, 2020. [6] R. Mihalcea and P. Tarau, “TextRank: Bringing order into texts,” in Proc. 2004 Conf. Empirical Methods in Natural Language Processing (EMNLP), Barcelona, Spain, 2004, pp. 404–411. [7] J. Zhang, Y. Zhao, M. Saleh, and P. J. Liu, “PEGASUS: Pre-training with extracted gap-sentences for abstractive summarization,” in Proc. 37th Int. Conf. Machine Learning (ICML), 2020, pp. 11328–11339. [8] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. 2019 Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, MN, USA, 2019, pp. 4171–4186. [9] P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang, “SQuAD: 100,000+ questions for machine comprehension of text,” in Proc. 2016 Conf. Empirical Methods in Natural Language Processing (EMNLP), Austin, TX, USA, 2016, pp. 2383–2392, doi: 10.18653/v1/D16-1264. [10] C.-Y. Lin, “ROUGE: A package for automatic evaluation of summaries,” in Text Summarization Branches Out, Barcelona, Spain, 2004, pp. 74–81.