Towards scalable efficient on-device ASR with transfer learning
<|reference_start|>Towards scalable efficient on-device ASR with transfer learning: Multilingual pretraining for transfer learning significantly boosts the robustness of low-resource monolingual ASR models. This study systematically investigates three main aspects: (a) the impact of transfer learning on model performance during initial training or fine-tuning, (b) the influence of transfer learning across dataset domains and languages, and (c) the effect on rare-word recognition compared to non-rare words. Our finding suggests that RNNT-loss pretraining, followed by monolingual fine-tuning with Minimum Word Error Rate (MinWER) loss, consistently reduces Word Error Rates (WER) across languages like Italian and French. WER Reductions (WERR) reach 36.2% and 42.8% compared to monolingual baselines for MLS and in-house datasets. Out-of-domain pretraining leads to 28% higher WERR than in-domain pretraining. Both rare and non-rare words benefit, with rare words showing greater improvements with out-of-domain pretraining, and non-rare words with in-domain pretraining.<|reference_end|>
@article{pandey2024towards,
    title={Towards scalable efficient on-device ASR with transfer learning},
    author={Laxmi Pandey, Ke Li, Jinxi Guo, Debjyoti Paul, Arthur Guo, Jay
  Mahadeokar, Xuedong Zhang},
    journal={arXiv preprint arXiv:2407.16664},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16664},
    primaryClass={cs.CL eess.AS}
}