Transcribing Educational Videos Using Whisper: A preliminary study on using AI for transcribing educational videos
Project Overview
The document explores the application of generative AI in education, focusing on automatic speech recognition (ASR) systems such as Whisper for transcribing educational videos. It emphasizes the significance of accurate transcripts in improving the learning experience, particularly for non-native speakers, as they facilitate better comprehension and engagement with the content. The evaluation of Whisper's transcription quality across 25 educational videos reveals its effectiveness in generating usable transcripts while also highlighting areas for further investigation in ASR technology's educational applications. Overall, the findings suggest that integrating ASR tools like Whisper can enhance educational accessibility and learning outcomes, paving the way for broader utilization of generative AI in diverse educational contexts.
Key Applications
Whisper ASR system for transcribing educational videos
Context: Used in e-learning environments, particularly beneficial for students consuming educational videos, including those who are non-native speakers.
Implementation: The study implemented Whisper to transcribe 25 educational videos from YouTube, comparing generated transcripts with baseline transcripts.
Outcomes: Improved accessibility of educational content through transcripts, especially for non-native speakers. The study provided insights into the quality of transcription and potential improvements in ASR systems.
Challenges: Issues with audio quality affecting transcription accuracy, particularly with non-professionally created content and voices from diverse ethnic backgrounds.
Implementation Barriers
Technical barrier
Inconsistencies in audio quality and clarity affect the performance of ASR systems, leading to high error rates in transcripts.
Proposed Solutions: Future work includes investigating audio normalization techniques, the impact of context in improving transcription accuracy, and broader datasets for effective evaluation.
Implementation barrier
Limited analysis scope due to the biased selection of videos used for evaluation, affecting the generalizability of the findings.
Proposed Solutions: Broader datasets and comparative studies among different ASR models are suggested for future research.
Project Team
Ashwin Rao
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Ashwin Rao
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18