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 the education sector, with a focus on automatic speech recognition (ASR) systems, particularly the Whisper tool, which is utilized for transcribing educational videos. It underscores the significance of accurate transcripts in enhancing the learning experience by making content more accessible and engaging for students. The study assesses Whisper's performance across different models, highlighting both its strengths and limitations, such as the necessity for improved audio quality and context awareness to ensure the accuracy of transcriptions from diverse video sources. Overall, the findings indicate that while generative AI tools like Whisper hold great potential for improving educational resources, there remain challenges that must be addressed to maximize their effectiveness in facilitating learning and comprehension.
Key Applications
Whisper Speech-to-Text
Context: Transcribing educational videos for e-learning purposes, targeting educators and learners.
Implementation: Utilized Whisper to generate transcripts from educational videos, comparing results with baseline transcripts from YouTube.
Outcomes: Improved efficiency in generating transcripts, with varying levels of quality depending on the model used. The study provides insights into the potential of ASR for educational content.
Challenges: Quality of transcripts varied significantly, especially with inaudible sections leading to higher error rates. The lack of context awareness in ASR results also diminished transcript reliability.
Implementation Barriers
Technical Barrier
Inconsistent audio quality in educational videos affects transcription accuracy, leading to errors due to inaudible segments and lack of context.
Proposed Solutions: Improving audio quality through better recording practices and using high-quality audio formats, and implementing features to mark inaudible segments while leveraging contextual information to enhance transcription accuracy.
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
Analysis Provider: Openai