SimulU: Training-free Policy for Long-form Simultaneous Speech-to-Speech Translation

Amir Djanibekov, Luisa Bentivogli, Matteo Negri, Sara Papi

SimulU: Training-free Policy for Long-form Simultaneous Speech-to-Speech Translation: 4 upvotes on Hugging Face Daily Papers, #18 of 32 papers on 2026-03-20. Day-by-day upvote history. It lost 12 votes when the Hub removed votes in bulk.

Simultaneous speech-to-speech translation (SimulS2S) is essential for real-time multilingual communication, with increasing integration into meeting and streaming platforms. Despite this, SimulS2S remains underexplored in research, where current solutions often rely on resource-intensive training procedures and operate on short-form, pre-segmented utterances, failing to generalize to continuous speech. To bridge this gap, we propose SimulU, the first training-free policy for long-form SimulS2S. SimulU adopts history management and speech output selection strategies that exploit cross-attention in pre-trained end-to-end models to regulate both input history and output generation. Evaluations on MuST-C across 8 languages show that SimulU achieves a better or comparable quality-latency trade-off against strong cascaded models. By eliminating the need for ad-hoc training, SimulU offers a promising path to end-to-end SimulS2S in realistic, long-form scenarios.

Paper page on Hugging Face · arXiv

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