Year: 2025
Type: conference-paper
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-3.3Bmodel 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-computationalaware 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 endto-end training.
Institution: Universitat de València
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412944348