  Creating Automatic Speech Recognition (ASR) systems that are robust and
resilient to classroom conditions is paramount to the development of AI tools
to aid teachers and students. In this work, we study the efficacy of continued
pretraining (CPT) in adapting Wav2vec2.0 to the classroom domain. We show that
CPT is a powerful tool in that regard and reduces the Word Error Rate (WER) of
Wav2vec2.0-based models by upwards of 10%. More specifically, CPT improves the
model's robustness to different noises, microphones, classroom conditions as
well as classroom demographics. Our CPT models show improved ability to
generalize to different demographics unseen in the labeled finetuning data.
