CPT-Boosted Wav2vec2.0: Towards Noise Robust Speech Recognition for Classroom Environments

Ahmed Adel Attia, Dorottya Demszky, Tolúlopé Ògúnrèmí, Jing Liu, Carol Y. Espy-Wilson

Abstract: 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 and classroom conditions.