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arXiv · 2601.22777

RASST: Retrieval-Augmented Simultaneous Speech Translation

Jiaxuan Luo·Siqi Ouyang·Jiaxing Xu·Lei Li
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Abstract

Simultaneous speech translation produces target text incrementally from partial speech input. Recent speech large language models have markedly improved SST quality but still struggle with rare and domain-specific terminology. Retrieval augmentation has helped in automatic speech recognition and neural machine translation, but extending it to SST is non-trivial: retrieval must be fast and accurate under partial speech, and the model must decide whether and when to apply retrieved terms during incremental generation. We propose Retrieval-Augmented Simultaneous Speech Translation (RASST), which addresses both challenges. For accurate cross-modal retrieval under partial input, RASST trains a lightweight speech-text retriever that produces chunkwise terminology hints for the Speech LLM via multi-scale retrieval. To use these hints correctly, we synthesize training data that teaches the Speech LLM to decide whether and when to apply each retrieved term. Experiments on ACL 60/60 dev set and the ESO test set show that RASST improves terminology accuracy by nearly 40% and overall translation quality by up to 3 BLEU points, with negligible computational overhead.

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Jiaxuan Luo, Siqi Ouyang, Jiaxing Xu, Lei Li. 2026-06-13. RASST: Retrieval-Augmented Simultaneous Speech Translation. https://arxiv.org/abs/2601.22777

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