Self-Train Before You Transcribe
<|reference_start|>Self-Train Before You Transcribe: When there is a mismatch between the training and test domains, current speech recognition systems show significant performance degradation. Self-training methods, such as noisy student teacher training, can help address this and enable the adaptation of models under such domain shifts. However, self-training typically requires a collection of unlabelled target domain data. For settings where this is not practical, we investigate the benefit of performing noisy student teacher training on recordings in the test set as a test-time adaptation approach. Similarly to the dynamic evaluation approach in language modelling, this enables the transfer of information across utterance boundaries and functions as a method of domain adaptation. A range of in-domain and out-of-domain datasets are used for experiments demonstrating large relative gains of up to 32.2%. Interestingly, our method showed larger gains than the typical self-training setup that utilises separate adaptation data.<|reference_end|>
@article{flynn2024self-train,
    title={Self-Train Before You Transcribe},
    author={Robert Flynn, Anton Ragni},
    journal={arXiv preprint arXiv:2406.12937},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12937},
    primaryClass={eess.AS cs.CL cs.LG cs.SD}
}