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A Case Study on Filtering for End-to-End Speech Translation
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A Case Study on Filtering for End-to-End Speech Translation
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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.
Forward citations
Cited by 1 Pith paper
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Leveraging Audio-LLMs to Filter Speech-to-Speech Training Data
An audio-LLM is trained with Rank-to-Distill pseudo-labels to filter speech pairs, improving end-to-end S2ST performance over unfiltered baselines on CVSS-C and SpeechMatrix.
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