# Transcribing Educational Videos Using Whisper: A preliminary study on using AI for transcribing educational videos

arXiv: https://arxiv.org/abs/2307.03200 · HTML: https://mlsanity.com/paper/2307.03200/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 14 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Flashlight: Enabling Innovation in Tools for Machine Learning](https://mlsanity.com/paper/2201.12465/) (2022)
- [PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit](https://mlsanity.com/paper/2205.12007/) (2022)
- [The Lockdown Effect: Implications of the COVID-19 Pandemic on Internet Traffic](https://mlsanity.com/paper/2008.10959/) (2020)

## Built on by (most cited first, 2 of 2)

- [Generative Artificial Intelligence in Learning Analytics: Contextualising Opportunities and Challenges through the Learning Analytics Cycle](https://mlsanity.com/paper/2312.00087/) (2023)
- [Towards an optimised evaluation of teachers' discourse: The case of engaging messages](https://mlsanity.com/paper/2412.14011/) (2024)

## Abstract (arXiv)

Videos are increasingly being used for e-learning, and transcripts are vital to enhance the learning experience. The costs and delays of generating transcripts can be alleviated by automatic speech recognition (ASR) systems. In this article, we quantify the transcripts generated by whisper for 25 educational videos and identify some open avenues of research when leveraging ASR for transcribing educational videos.