A Case Study on Filtering for End-to-End Speech Translation
<|reference_start|>A Case Study on Filtering for End-to-End Speech Translation: It is relatively easy to mine a large parallel corpus for any machine learning task, such as speech-to-text or speech-to-speech translation. Although these mined corpora are large in volume, their quality is questionable. This work shows that the simplest filtering technique can trim down these big, noisy datasets to a more manageable, clean dataset. We also show that using this clean dataset can improve the model's performance, as in the case of the multilingual-to-English Speech Translation (ST) model, where, on average, we obtain a 4.65 BLEU score improvement.<|reference_end|>
@article{alam2024a,
    title={A Case Study on Filtering for End-to-End Speech Translation},
    author={Md Mahfuz Ibn Alam and Antonios Anastasopoulos},
    journal={arXiv preprint arXiv:2402.01945},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01945},
    primaryClass={cs.CL}
}