Abstract

Analyzing teachers' discourse plays a fundamental role in educational research and is a key component of Teaching Analytics. This usually involves transcribing lessons from audio recordings. As the number of recordings grows, Automatic Speech Recognition (ASR) systems gain popularity as a means for transcribing these recordings. However, most ASR systems are trained over very specific domains which usually involve read text and low environmental noise. This suggests common ASR systems available on the market may underperform over classroom recordings, as they present a unique type of environmental sound and spontaneous discourse, as opposed to the usual training domains. To address this challenge we present a system that automatically transcribes classroom discourse in a robust way with regard to classroom noise, which was trained over few annotated data. In particular, we used a state-of-the-art ASR model based on wav2vec 2.0 and fine-tuned it over a 6-h dataset of 4th to 8th grade Chilean lessons. We found that by leveraging its transformer-based architecture and changing the fine-tuning domain to classroom recordings, we can obtain a more accurate and robust transcriber for this source of audio which outperforms other popular cloudbased systems up to 35% and 59% in terms of Word and Character Error Rates, respectively. This work contributes by using state-of-the-art ASR techniques to develop a tool which is particularly adapted to classroom environments, making it robust and more reliable with regard to their environmental sound and the way teaching discourse is carried out.