MLLP-VRAIN UPV system for the IWSLT 2025 Simultaneous Speech Translation Translation task

• URL: http://arxiv.org/abs/2506.18828v1
• Date: Mon, 23 Jun 2025 16:44:01 GMT
• Title: MLLP-VRAIN UPV system for the IWSLT 2025 Simultaneous Speech Translation Translation task
• Authors: Jorge Iranzo-Sánchez, Javier Iranzo-Sánchez, Adrià Giménez, Jorge Civera, Alfons Juan,
• Abstract summary: This work describes the participation of the MLLP-VRAIN research group in the shared task of the IWSLT 2025 Simultaneous Speech Translation track.<n>Our submission addresses the unique challenges of real-time translation of long-form speech by developing a modular cascade system.
• Score: 7.247809853198223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work describes the participation of the MLLP-VRAIN research group in the shared task of the IWSLT 2025 Simultaneous Speech Translation track. Our submission addresses the unique challenges of real-time translation of long-form speech by developing a modular cascade system that adapts strong pre-trained models to streaming scenarios. We combine Whisper Large-V3-Turbo for ASR with the multilingual NLLB-3.3B model for MT, implementing lightweight adaptation techniques rather than training new end-to-end models from scratch. Our approach employs document-level adaptation with prefix training to enhance the MT model's ability to handle incomplete inputs, while incorporating adaptive emission policies including a wait-$k$ strategy and RALCP for managing the translation stream. Specialized buffer management techniques and segmentation strategies ensure coherent translations across long audio sequences. Experimental results on the ACL60/60 dataset demonstrate that our system achieves a favorable balance between translation quality and latency, with a BLEU score of 31.96 and non-computational-aware StreamLAAL latency of 2.94 seconds. Our final model achieves a preliminary score on the official test set (IWSLT25Instruct) of 29.8 BLEU. Our work demonstrates that carefully adapted pre-trained components can create effective simultaneous translation systems for long-form content without requiring extensive in-domain parallel data or specialized end-to-end training.

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