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Cascade versus Direct Speech Translation: Do the Differences Still Make a Difference?

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arxiv 2106.01045 v1 pith:PJN4E6SW submitted 2021-06-02 cs.CL

classification cs.CL
keywords cascadecloseddifferencesdirectfirstparadigmsspeechtranslation
verification ladder T0 review T1 audit T2 compute T3 formal
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Five years after the first published proofs of concept, direct approaches to speech translation (ST) are now competing with traditional cascade solutions. In light of this steady progress, can we claim that the performance gap between the two is closed? Starting from this question, we present a systematic comparison between state-of-the-art systems representative of the two paradigms. Focusing on three language directions (English-German/Italian/Spanish), we conduct automatic and manual evaluations, exploiting high-quality professional post-edits and annotations. Our multi-faceted analysis on one of the few publicly available ST benchmarks attests for the first time that: i) the gap between the two paradigms is now closed, and ii) the subtle differences observed in their behavior are not sufficient for humans neither to distinguish them nor to prefer one over the other.

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  1. IIITH-BUT system for IWSLT 2025 low-resource Bhojpuri to Hindi speech translation

    cs.CL 2025-06 conditional novelty 4.0 of 10

    Fine-tuning SeamlessM4T on 20 hours of Bhojpuri-Hindi data with tuned hyperparameters and SpecAugment reaches 36.4 dev BLEU but only 9.9 test BLEU in the IWSLT 2025 low-resource task.

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