A Case Study on Filtering for End-to-End Speech Translation
Md Mahfuz Ibn Alam Antonios Anastasopoulos
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.