Continued Pretraining for Domain Adaptation of Wav2vec20 in Automatic Speech Recognition for Elementary Math Classroom Settings
<|reference_start|>Continued Pretraining for Domain Adaptation of Wav2vec20 in Automatic Speech Recognition for Elementary Math Classroom Settings: 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.<|reference_end|>
@article{attia2024continued,
    title={Continued Pretraining for Domain Adaptation of Wav2vec2.0 in Automatic
  Speech Recognition for Elementary Math Classroom Settings},
    author={Ahmed Adel Attia, Dorottya Demszky, Tolulope Ogunremi, Jing Liu, Carol
  Espy-Wilson},
    journal={arXiv preprint arXiv:2405.13018},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13018},
    primaryClass={cs.CL cs.AI eess.AS}
}