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

Antonios Anastasopoulos; Md Mahfuz ibn Alam

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

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arxiv 2402.01945 v1 pith:AYACQVRS submitted 2024-02-02 cs.CL

Md Mahfuz Ibn Alam , Antonios Anastasopoulos This is my paper · ORCID

classification cs.CL

keywords translationcasecleandatasetfilteringlargemodelspeech

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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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FAMA: The First Large-Scale Open-Science Speech Foundation Model for English and Italian
cs.CL 2025-05 conditional novelty 6.0 of 10

FAMA provides the first large-scale, fully open-source-licensed speech foundation models for English and Italian, with competitive accuracy and much higher speed than existing models.

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