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.
@article{arxiv.2402.01945,
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}
}