CPT-Boosted Wav2vec20: Towards Noise Robust Speech Recognition for Classroom Environments
<|reference_start|>CPT-Boosted Wav2vec20: Towards Noise Robust Speech Recognition for Classroom Environments: 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.<|reference_end|>
@article{attia2024cpt-boosted,
    title={CPT-Boosted Wav2vec2.0: Towards Noise Robust Speech Recognition for
  Classroom Environments},
    author={Ahmed Adel Attia, Dorottya Demszky, Tolulope Ogunremi, Jing Liu, Carol
  Espy-Wilson},
    journal={arXiv preprint arXiv:2409.14494},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14494},
    primaryClass={cs.CL cs.LG cs.SD eess.AS}
}