A Case Study on Filtering for End-to-End Speech Translation

• URL: http://arxiv.org/abs/2402.01945v1
• Date: Fri, 2 Feb 2024 22:42:33 GMT
• Title: A Case Study on Filtering for End-to-End Speech Translation
• Authors: Md Mahfuz Ibn Alam and Antonios Anastasopoulos
• Abstract summary: 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. This work shows that the simplest filtering technique can trim down these big, noisy datasets to a more manageable, clean dataset.
• Score: 32.676738355929466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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