Artificial intelligence inspired multilanguage framework for note-taking and qualitative content-based analysis of lectures

With the advent of technology and digitization, the use of Information and Communication Technology (ICT) and its tools for the imperative dissemination of information to learners are gaining more ground. During the process of the conveyance of lectures, it is mostly observed that students (learners) are supposed to take notes (minutes) of the subject matter being delivered to them. The existence of different factors like disturbance (noise) from the environment, learner’s lack of interest, problems with the tutor’s voice, and pronunciation, or others, may hinder the practice of preparing (or taking) lecture notes effectively. To tackle such an issue, we propose an artificial intelligence-inspired multilanguage framework for the generation of the lecture script (of complete) and minutes (only important contents) of the lecture (or speech). We also aimed to perform a qualitative content-based analysis of the lecture’s content. Furthermore, we have validated the performance(accuracy) of the proposed framework with that of the manual note-taking method. The proposed framework outperforms its counterpart in terms of note-taking and performing the qualitative content-based analysis. In particular, this framework will assist the tutors in getting insights into their lecture delivery methods and materials. It will also help them improvise to a better approach in the future. The students will be benefited from the outcomes as they do not have to invest valuable time in note-taking/preparation.